RNA-seq is a revolutionary tool for transcriptomics that has advantages over previous methods like microarrays. It allows for single-base resolution expression profiling, detection of splicing variants and gene fusions, and can detect a wider dynamic range of expression levels. RNA-seq is being used to improve genome annotations by characterizing alternative splicing events and verifying gene boundaries. It is also useful for generating genetic resources for non-model species by performing de novo transcriptome sequencing and annotation. Additionally, RNA-seq can help advance proteomics by providing a reference database to match peptide spectra. Studies are using RNA-seq to examine spatial and temporal transcriptome landscapes in various plants.
Genotyping by Sequencing is a robust,fast and cheap approach for high throughput marker discovery.It has applications in crop improvement programs by enhancing identification of superior genotypes.
A workshop is intended for those who are interested in and are in the planning stages of conducting an RNA-Seq experiment. Topics to be discussed will include:
* Experimental Design of RNA-Seq experiment
* Sample preparation, best practices
* High throughput sequencing basics and choices
* Cost estimation
* Differential Gene Expression Analysis
* Data cleanup and quality assurance
* Mapping your data
* Assigning reads to genes and counting
* Analysis of differentially expressed genes
* Downstream analysis/visualizations and tables
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Genotyping by Sequencing is a robust,fast and cheap approach for high throughput marker discovery.It has applications in crop improvement programs by enhancing identification of superior genotypes.
A workshop is intended for those who are interested in and are in the planning stages of conducting an RNA-Seq experiment. Topics to be discussed will include:
* Experimental Design of RNA-Seq experiment
* Sample preparation, best practices
* High throughput sequencing basics and choices
* Cost estimation
* Differential Gene Expression Analysis
* Data cleanup and quality assurance
* Mapping your data
* Assigning reads to genes and counting
* Analysis of differentially expressed genes
* Downstream analysis/visualizations and tables
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
AGRF in conjunction with EMBL Australia recently organised a workshop at Monash University Clayton. This workshop was targeted at beginners and biologists who are new to analysing Next-Gen Sequencing data. The workshop also aimed to provide users with a snapshot of bioinformatics and data analysis tips on how to begin to analyse project data. An introduction to RNA-seq data analysis was presented by AGRF Senior Bioinformatician Dr. Sonika Tyagi.
Presented: 1st August 2012
A concise and well fabricated presentation the current techniques used for plant genome editing including CRISPER/cas9 system, TALENS, TELES, ZINC FINGER NUCLEASES(ZFN), HEJ (homologous endjoing) and many other high throughout techniques along references.
Genomics and its application in crop improvementKhemlata20
meaning ,definition of genome ,genomics ,tools of genomics ,what is genome sequencing ,methods of genome sequencingand genome mapping ,advantage of genomics over traditional breeding program, examples of some crops whose genome has been sequenced, important points about genomics, work in the field of genomics ,applications of genomics .classification of genomics .different Omics in genomics like Proteomics ,Transcriptomics ,Metabolomics ,Need of genome sequencing
Targeted Induced Local Lesions IN Genome. Mutations (Single base pair substitution) are created by traditionally used chemical mutagens. Identify SNPs and / or INDELS in a gene / genes of interest from a mutagenized population.
Course: Bioinformatics for Biomedical Research (2014).
Session: 4.1- Introduction to RNA-seq and RNA-seq Data Analysis.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
Introduction:
Proposed by Meuwissen et al. (2001)
GS is a specialized form of MAS, in which information from genotype data on marker alleles covering the entire genome forms the basis of selection.
The effects associated with all the marker loci, irrespective of whether the effects are significant or not, covering the entire genome are estimated.
The marker effect estimates are used to calculate the genomic estimated breeding values (GEBVs) of different individuals/lines, which form the basis of selection.
Why to go for genomic selection:
Marker-assisted selection (MAS) is well-suited for handling oligogenes and quantitative trait loci (QTLs) with large effects but not for minor QTLs.
MARS attempts to take into account small effect QTLs by combining trait phenotype data with marker genotype data into a combined selection index.
Based on markers showing significant association with the trait(s) and for this reason has been criticized as inefficient
The genomic selection (GS) scheme was to rectify the deficiency of MAS and MARS schemes. The GS scheme utilizes information from genome-wide marker data whether or not their associations with the concerned trait(s) are significant.
GEBV: GenomicEstimated Breeding Values-
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Gene-assisted genomic selection:
A GS model that uses information about prior known QTLs, the targeted QTLs were accumulated in much higher frequencies than when the standard ridge regression was used
The sum total of effects associated with all the marker alleles present in the individual and included in the GS model applied to the population under selection
Calculated on a single individual basis
Population used:
Training population: used for training of the GS model and for obtaining estimates of the marker-associated effects needed for estimation of GEBVs of individuals/lines in the breeding population.
Breeding population: the population subjected to GS for achieving the desired improvement and isolation of superior lines for use as new varieties/parents of new improved hybrids.
Training population-
large enough: must be representative of the breeding population: max. trait variance with marker : by cluster analysis
should have either equal or comparable LD, LD decay rates with breeding populations
Updated by including individuals/lines from the breeding population
Training more than one generation
Low colinearity between markers is needed since high colinearity tends to reduce prediction accuracy of certain GS models. (colinearity disturbed by recombination)
AGRF in conjunction with EMBL Australia recently organised a workshop at Monash University Clayton. This workshop was targeted at beginners and biologists who are new to analysing Next-Gen Sequencing data. The workshop also aimed to provide users with a snapshot of bioinformatics and data analysis tips on how to begin to analyse project data. An introduction to RNA-seq data analysis was presented by AGRF Senior Bioinformatician Dr. Sonika Tyagi.
Presented: 1st August 2012
A concise and well fabricated presentation the current techniques used for plant genome editing including CRISPER/cas9 system, TALENS, TELES, ZINC FINGER NUCLEASES(ZFN), HEJ (homologous endjoing) and many other high throughout techniques along references.
Genomics and its application in crop improvementKhemlata20
meaning ,definition of genome ,genomics ,tools of genomics ,what is genome sequencing ,methods of genome sequencingand genome mapping ,advantage of genomics over traditional breeding program, examples of some crops whose genome has been sequenced, important points about genomics, work in the field of genomics ,applications of genomics .classification of genomics .different Omics in genomics like Proteomics ,Transcriptomics ,Metabolomics ,Need of genome sequencing
Targeted Induced Local Lesions IN Genome. Mutations (Single base pair substitution) are created by traditionally used chemical mutagens. Identify SNPs and / or INDELS in a gene / genes of interest from a mutagenized population.
Course: Bioinformatics for Biomedical Research (2014).
Session: 4.1- Introduction to RNA-seq and RNA-seq Data Analysis.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
Part 1 of RNA-seq for DE analysis: Defining the goalJoachim Jacob
First part of the training session 'RNA-seq for Differential expression' analysis. We explain how we can detect differential expression based on RNA-seq data. Interested in following this session? Please contact http://www.jakonix.be/contact.html
RNA-Seq analysis of blueberry fruit identifies candidate genes involved in ri...Ann Loraine
I presented these slides at the Plant Metabolic Network workshop held at the Plant Animal Genome Conference (PAG) XXII, January, 2014. The main goals of the talk were to describe RNA-Seq based annotation of a blueberry genome assembly and explain how we used PlantCyc enzyme data to associate blueberry genes with metabolic pathways.
RNA Sequence data analysis,Transcriptome sequencing, Sequencing steady state RNA in a sample is known as RNA-Seq. It is free of limitations such as prior knowledge about the organism is not required.
RNA-Seq is useful to unravel inaccessible complexities of transcriptomics such as finding novel transcripts and isoforms.
Data set produced is large and complex; interpretation is not straight forward.
Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...Bioo Scientific
microRNAs (miRNAs) may provide useful markers for the development of disease diagnostic and prognostic assays. NGS brings sensitivity, specificity, and the ability to maximize data acquisition and minimize costs of miRNA sequencing by using multiplex strategies to allow many samples to be sequenced simultaneously with small RNA analysis. However, small RNA sequencing has typically suffered from three major drawbacks: severe bias, such that sequencing data does not reflect original miRNA abundances, the need to gel purify final libraries, and lack of low-input protocols. The NEXTflex™ Small RNA-Seq Kit v3 addresses these drawbacks by using two strategies: randomized adapters to reduce ligation-associated bias, and a dual approach to adapter-dimer reduction, thereby allowing gel-free or low-input small RNA library preparation.
Correcting bias and variation in small RNA sequencing for optimal (microRNA) ...Christos Argyropoulos
Presentation given about the Generalized Additive Model Location, Scale and Shape (GAMLSS) methodology for the analysis of small RNA sequencing data and the potential of microRNAs as biomarkers for kidney and cardiometabolic diseases
description of functional genomics and structural genomics and the techniques involved in it and also decribing the models of forward genetics and techniques involved in it and reverse genetics and techniques involved in it
this is a presentation on molecular markers that include what is molecular marker, it's types, biochemical markets (alloenzyme), it's classification, data analysis and it's applications
Please describe in detaila. How to create and to use a transpo.pdf4babies2010
Please answer the discussion questions and TYPE you answers. MINICASE ARE YOU
REALLY BUYING AMERICAN? Consider the follawing scenario of a typical\" American fam
work, stopping for gas at the Shell station. At the grocery ily: The Osbornes, Jesse and Ann, live
in the suburbs of store, she fills her cart with a variety of items, including Chicago Jesse is a
manager at Trader Joe\'s specialty grocery Ragu spaghetti sauce, Hellmann\'s mayonnaise,
Carnation store chain. Ann is an edvertising executive for Leo Burnett Instant Breakfast drink, a
case of Arrowhead water, CoffeeMate nondairy coffee creamer, Chicken-of the-Sea Ann listens
to the new Adam Lambert CD on her Alpine car stereo in her Jeep Cherakes while driving homa
from canned tuna, Lipton tea, a hall-dozen cans of Slim-Fast. Dannon yogurt, and several
packages of Stouffer\'s Lean dinners and some Hot Packats. For a treat. Groupe Danone of
France produces Dannon yogurt she picks up some Ben & Jerry\'s ice cream, Toll House cook-
es, and a Butterfinger candy bar. She also grabs several cans af Alpo for thair dog, Sassy, and a
box of Friskies and a bag of Tidy Cat cat litter for their cat, Lily. She goes down the toilet- ries
aisle for some Dove soap, Q-tips cotton swabs, Vaseline Samsung smartphones are made by
South Korea\'s lip gloss, and Jergen\'s moisturizing lotion. Before fhng,Samsung- she calls Jesse
on her Samsung smartphone to see whetherBertelsmann AG of Germany owns 53 percent of
there is anything else he needs. Ho asks her to pick up some PowerBars for him to take to the
gym during his lunchtime the remaining 47 percent. workouts next week. She also stops at the
bookstore andSociete Bic of France produces Bic pens picks up the new John Grisham book
published by Random House, signing the credit card slip with her Bic pen. Chicken-of the Sea
tuna is made by Chicken of the Sea International, which is besed in Thailand. Japan\'s Kao owns
Jergen\'s. Penquin Random House, and Pearson plc of the UK owns ·Japan\'s Bridgestone
Corporation owns Firestone. BP of the United Kingdom owns BP gas stations. After leaving his
office, Jesse stops at the BP gas station to fili his gas tank and checks the air pressure in his
Firestone tires. He heads to the liquor store for a cese of Miller Genuine thSpiderman movies
Sony Pictures Tolovision distrib- Draft beer and a bottle of Wild Turkey bourbon. He walks next
door to the sporting goods store to pick up someSABMiller plc of the United Kingdom produces
Miller Columbia Pictures, owned by Sony of Japan, released utes Breaking Bad. Wilson
racquetballs for his workouts next week. boer, Ann\'s favorite TV show, Breaking Bad, is just
startinDavide Campari of Italy awns the Wild Turkay brand. comes in the door, so she pours
hersalf a glass af Amer Group of Finland owns Wilsan Sporting Goods. and turns on their Philips
high-. Baringer Winery of Napa, California, is owned by definition talevision while Jasse
prepares dinner loads the latest Spiderman inst.
Presentation1..gymno..non specific markers n microsatellites..by Nikita Patha...NIKITAPATHANIA
NON-SPECIFIC MARKERS-“A cloned random DNA fragment whose function or specific features are not known e.g. AFLP, RAPD, IRAP, SSR etc.
These marker type generally measure apparently neutral DNA variations.
They are generally the PCR based molecular markers.
Determining genetic diversity can be based on morphological, biochemical, and molecular types of information.
However, molecular markers have advantages over other kinds, where they show genetic differences on a more detailed level without interferences from environmental factors, and where they involve techniques that provide fast results detailing genetic diversity.
MOLECULAR MARKERS - In genetics, a molecular marker (identified as genetic marker) is a fragment of DNA that is associated with a certain location within the genome.
MICROSATELLITES : Microsatellites are tandem repeats(TRs) of 1–6 bp and are also known as simple sequence repeats (SSRs). Microsatellites are TRs of base pairs that are widely spread throughout the genome. Microsatellites are located in the coding and non coding regions.
Microsatellite markers are codominant, abundant, and multiallelic and play an important role in the study of molecular population genetics, positional cloning, QTL mapping, disease identification, pathogenesis, and evolutionary studies, etc .
Major molecular markers based on assessment of variability generated by microsatellites sequences are:
STMSs (Sequence Tagged Microsatellite Site), SSLPs (Simple Sequence Length Polymorphism), SNPs (Single Nucleotide Polymorphisms), SCARs (Sequence Characterized Amplified Region) and CAPS (Cleaved Amplified Polymorphic Sequences).
SINGLE COPY NUCLEAR GENE MARKER – Has one physical location in the genome and can have orthologs in different species.
Comprise of a unique sequence that code for proteins and undergo transcription.
Seed plants probably comprise 260,000 to 310,000 extant species. Current seed plants consist of angiosperms and gymnosperms, the latter of which are further sub divided into Cycadidae, Ginkgoidae, Gnetidae, and Pinidae .
In contrast to angiosperms, for which several genomic, transcriptomic and phylogenetic resources are available, there are few, if any, molecular markers that allow broad comparisons among gymnosperm species.
With few gymnosperm genomes available, recently obtained transcriptomes in gymnosperms are a great addition to identifying single-copy gene families as molecular markers for phylogenomic analysis in seed plants.
Taking advantage of an increasing number of available genomes and transcriptomes, there is identification of single-copy genes in a broad collection of seed plants and used these to infer phylogenetic relationships between major seed plant taxa.
All studied seed plants shared 1,469 single-copy genes, which are generally involved in functions like DNA metabolism, cell cycle, and photosynthesis.
The analysis of global gene expression and transcription factor regulation, global approaches to alternative splicing and its regulation, long noncoding RNAs, gene expression models of signalling pathways, from gene expression to disease phenotypes, introduction to isoform sequencing, systematic and integrative analysis of gene expression to identify feature genes underlying human diseases.
Course: Bioinformatics for Biomedical Research (2014).
Session: 2.1.3- Next Generation Sequencing. Technologies and Applications. Part III: NGS Applications II.
Statistics and Bioinformatisc Unit (UEB) & High Technology Unit (UAT) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.
Molecular Systematics provides a solid conceptual basis for the evolutionary history of organisms. Molecular systematics is the study of DNA and RNA sequences to infer evolutionary links across organisms. Molecular approaches/ techniques provide excellent resources for the study of evolution and phylogeny.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
1. Manjappa
Ph. D. Scholar
Dept. of Genetics & Plant
Breeding
UAS, GKVK, Bengaluru, India
Catalyzing plant science research with RNA-seq
1
2. Central dogma of molecular biology
Transcriptome
(mRNA, rRNA, tRNA, and
other non-coding RNA)
2
3. Why to study transcriptome ?
It reflects the genes that are being actively expressed at any
given time (expression profiling)
Expression level of mRNAs in a given cell population varies
How an organism adapt to the developmental cues and
environmental fluctuations.
3
4. Quantify the changing expression levels of each transcript
during development and under different conditions
Catalogue all species of transcript (mRNAs, non-coding
RNAs & small RNAs)
Determine the transcriptional structure of genes, in terms
of their start sites, 5′ and 3′ ends, splicing patterns and
other post-transcriptional modifications
Aim of transcriptomics
4
6. Limitation in microarray technique
Reliance upon knowledge of genome sequence
High background levels owing to cross hybridization
Limited dynamic range of detection owing to background &
saturation of signals
Comparing expression levels across different experiments is often
difficult & can require complicated normalization methods
Sanger sequencing of cDNA or EST libraries:
- Relatively low throughput, expensive & generally not quantitative
Tag-based methods (SAGE, CAGE & MPSS):
high throughput & precise, ‘digital’ gene expression levels
Most are based on expensive Sanger sequencing technology, & a
significant portion of the short tags cannot be uniquely mapped
to the reference genome
only a portion of the transcript is analyzed and isoforms are
generally indistinguishable from each other
6
7. Wang et. al, RNA-Seq: a revolutionary tool for transcriptomics, Nat. Rev. Genetics 10, 57-63, 2009).
Next generation sequencing (NGS)
Sample preparation
Data analysis:
Mapping reads
Visualization (Gbrowser)
De novo assembly
Quantification
RNA-sequencing
7
8. RNA-seq vs. microarray
• RNA-seq can be used to characterize novel transcripts and splicing
variants as well as to profile the expression levels of known
transcripts (but hybridization-based techniques are limited to detect
transcripts corresponding to known genomic sequences)
• Detect large dynamic range of expression levels (9,000 fold)
compared to microarray (100-few-hundred fold
• RNA-seq has higher resolution than whole genome tiling array
analysis
• In principle, mRNA can achieve single-base resolution, where
the resolution of tiling array depends on the density of probes
• High levels of reproducibility, for both technical and biological
replicates
• RNA-seq can apply the same experimental protocol to various
purposes, whereas specialized arrays need to be designed in these
cases
• Detecting SNPs (needs SNP array otherwise)
• Mapping exon junctions (needs junction array otherwise)
• Detecting gene fusions (needs gene fusion array otherwise)
8
9. RNA-seq and microarray agree fairly well only for
genes with medium levels of expression
Saccharomyces cerevisiae cells grown in nutrient-rich media. Correlation is very low
for genes with either low or high expression levels.
9
11. RNA Seq helps to look at
Alternative gene spliced transcripts
Post-transcriptional modifications
Gene fusion
Mutations/SNPs
Changes in gene expression
• Used to determine exon/intron boundaries
• Verify or amend previously annotated 5’ and 3’ gene
boundaries.
• Also includes miRNA, tRNA, and rRNA profiling
11
13. Library construction
RNA fragmentation (RNA
hydrolysis or nebulization) &
cDNA fragmentation (DNase I
treatment or sonication)
Bioinformatic challenges
Devt. of efficient methods to
store, retrieve and process
large amounts of data, which
must reduce errors in image
analysis and base-calling and
remove low-quality reads.
13
Challenges for RNA-Seq
bias at depleted 5′ and 3′ ends
bias at 3′ ends
15. • First draft of Arabidopsis thaliana genome sequence (2000); its
annotation continues to be improved
• Large amounts of Sanger sequencing-generated EST data provided the
initial basis for gene identification and expression profiling
Expensive, time consuming, inherently biased against low-
abundance transcripts & are typically enriched in transcript
termini
• RNA-seq circumvents these limitations and provides accurate
resolution of splice junctions and alternative splicing events
• Arabidopsis transcriptome survey using Illumina shows
- At least 42% of intron-containing genes are alternatively
spliced (Filichkin et al., 2010)
- 61% when only multi-exonic genes are sampled
- ~48% of rice genes (Lu et al.,2010)
1. IMPROVING GENOME ANNOTATION WITH TRANSCRIPTOMIC DATA
15
16. Contd…
• Mining RNA seq data in search of TSS variation is improving gene
structure annotation and alternative TSSs have been detected in
∼10,000 loci in Arabidopsis and rice
(Tanaka et al., 2009).
• An ideal genome annotation would identify
Genes that show invariant transcript sequences
Those that exhibit alternative splicing and
Link these events to specific spatial, temporal, developmental,
and/or environmental cues.
• Abiotic stress in Arabidopsis can increase or decrease the proportions of
apparently unproductive isoforms for some key regulatory genes,
supports alternative splicing is an important mechanism in the
regulation of gene function
(Filichkin et al., 2010)
16
17. Contd…
• Polymorphisms between different A. thaliana accessions is
one SNP per ∼200 bp.
• Complete re-sequencing of the transcriptomes and
annotation of different accessions helps to interpret the
functional consequences of polymorphism
• Utilizing genomic and transcriptomic data for in silico gene
prediction results in a more reliable annotated genome,
with Information on SNPs, indels, splice variants and
expression variation
17
18. Generating genomic and enabling proteomic
resources for “non-model” species
• Published plant genome sequences represents very small fraction of plant
taxonomic diversity
• Study of “non-model” species challenging
• de novo sequencing of the transcriptome to generate genetic resources
1. Eucalyptus (mizrachi et al., 2010)
2. Garlic (sun et al., 2012)
3. Pea (franssen et al., 2011),
4. Chestnut (barakat et al., 2009)
5. Chickpea (garg et al., 2011)
6. Olive (alagna et al., 2009)
7. Safflower (lulin et al., 2012
8. Japanese knotweed (Hao et al., 2011).
Gene annotation relies on identifying homologs, & ideally orthologs, in
species with an annotated genome (if no appropriate EST databases are
available)
If not, A. thaliana genome sequence (Gold std.)
Further confirmation; interrogating additional plant databases
Annotation with pre-existing EST database Eg: melon (Dai et al., 2011)
Same
function
different
function
18
19. • De novo RNA-seq to identify genetic polymorphisms
(molecular breeding), wherein multiple cultivars or close-
related species with variations in traits of interest are
sequenced and genetic variation is identified.
Allows generation of molecular markers to facilitate
progeny selection and molecular genetics research
Ex: 12,000 SSRs in a single RNA-seq analysis of sesame
(earlier only 80 SSRs), on average 1 genic-SSR per ∼8 kb
(Zhang et al., 2012)
5,234 SNPs in transcriptomes of five winter rye inbred
lines. Used in a high-throughput SNP genotyping array
(Haseneyer et al., 2011)
• Comparative sequence analysis of radish RNA- seq data and
Brassica rapa genome sequence lead to the discovery of
14,641 SSRs
Contd…
(Wang et al., 2012)
19
20. RNA Seq application to advance the field of
proteomics.
• Effective proteome profiling is generally considered to
depend heavily on the availability of a high-quality DNA
reference database
• High-throughput mass spectrometry-based protein
identification relies on the availability of an extensive DNA
sequence database in order to match experimentally
determined peptide masses with the theoretical proteome
generated by computationally translating transcripts
• RNA- seq based transcriptome profiling can provide an
effective data set for proteomic analysis of non-model
organisms
20
21. “RNA-Seq, facilitates the
matching of peptide mass
spectra with cognate gene
sequence”
• To test this, quantitative
analysis of the proteomes of
pollen from domesticated
tomato (Solanum
lycopersicum) and two wild
relatives
• RNA-Seq (454
pyrosequencing); >1200
proteins were identified
No major qualitative or quantitative differences were observed in the characterized proteomes
either with a highly curated community database of tomato sequences or the RNA-Seq database21
22. Characterizing temporal, spatial, regulatory,
and evolutionary transcriptome landscapes
Temporal transcriptome
• RNA-seq is increasingly being adopted to examine
transcriptional dynamics
• Ananalysis of transcriptome of grape berries during three
stages of devt. identified >6,500 genes that were expressed
in a stage-specific manner (Zenoni et al.,2010)
• Radish >21,000 genes differentially expressed at two
developmental stages of roots, includes genes strongly
linking root development with starch and sucrose
metabolism and with phenylpropanoid biosynthesis.
(Wang et al., 2012)
22
23. Objective: To understand the molecular mechanisms underlying tuberous root
formation and development.
• Radish (R. sativus) cultivar ‘Weixianqing’.
• Samples; cultivar ‘Weixianqing’.
• hypocotyl (1 cm, 7DAS) & true root (1 cm, 20 DAS (RLSS, the stage of cortex splitting),
10 seedlings of each were pooled together
• Illumina paired-end sequencing technology GAII platform (BGI; Shenzhen, China)
• Gene annotation: Comparative genome analysis between radish and Brassica rapa.
Unigenes were aligned with sequences in NCBI non-redundant protein (Nr) database,
Swiss-Prot protein database, Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathway database & Cluster of Orthologous Groups (COG) database using BLASTx
Annotation by using Blast2GO program
23
24. • Sequence similarity search was conducted against the NCBI
Nr (85.51 %), Nt (90.18%) and Swiss-Prot protein databases
(54%) using the BLASTx algorithm
• 21,109 unigenes were assigned GO terms.
Functional annotation of all non-redundant unigenes
Gene Ontology classification of assembled unigenes
(9,271; 43.92 %)
24
25. Transcript differences between RESS and RLSS
13,453
8,389
To understand the functions of
DEGs, mapped all the DEGs to
terms in the KEGG database &
found 29 pathways were
significantly enriched.
carbohydrate, energy, lipid,
amino acid, other amino acids,
terpenoids and polyketides,
metabolism and biosynthesis
of other secondary metabolites
20 (starch and sucrose metabolism) and 25 (phenylpropanoid
biosynthesis) unigenes significantly up-regulated, play a critical roles in
regulating radish tuberous root formation. Also confirm finding of radish
root is rich with carbohydrates and phenolic compounds. 25
starch and sucrose metabolism
(303) and phenylpropanoid
biosynthesis (177) two
predominant groups
27. • Previous gene expression studies using EST sequencing, spotted
microarrays & Affymetrix Gene Chip tech. (based on prior sequence)
• Provides only a fragmented picture of transcript accumulation patterns.
• RNA Seq to 7 tissues (leaf, flower, pod, two stages of pod-shell, root,
nodule) & 7 seed devt. Stages of BC5F5 plant G. max
• Compare transcript reads with recent genome sequence (assembly
Glyma1.01)
• Potential model for future RNA-Seq atlases
27
28. Mapping of short-read sequences:
• Illumina Genome analyser-II: produced 5.8-8.9 mill. 36-bp reads
for 7 non-seed tissues & 2.7-9.6 mill. 36-bp reads for seed tissues
• Alignment program GSNAP was used to map the reads to two
reference genomes: G. max and Bradyrhizobium japonicum.
• Digital gene expression analysis: 46,430 genes identified as “high
confidence” (correlation to full length cDNAs, ESTs, homology, &
ab initio methods)
• Of which 41,975 (90.4%) genes were transcriptionally active
Expression and gene structure
Coding regions of transcriptionally inactive genes were smaller and had a lower GC content
28
29. Hierarchical clustering of transcriptional
profiles in 14 tissues.
Tissue-specific analysis of the soybean transcriptome
Relative expression levels based on Z-score
analysis (3.4-3.6 more tissue specific)
early seed devt. stages late seed devt. stages
aerial tissues underground tissues
Z = (X-μ)/sd
Tissue
specific
Tissue
specific
29
30. Heatmap of the Legume Specific GenesHeatmap of top 500 highest expressed genes
Some legume specific genes
have tissue specific transcription
Glyma06g08290
Glyma04g08220 (Oleosin)
Glyma02g01590 (lectin precursor 1)
30
31. General trends in expression profiles for all genes tissue by tissue comparison (Fishers Exact test)
Higher transcriptional level
Importance: Understand Gene functions and molecular process occur during two stages.
GOslim analysis; between Seed 25 DAF & seed 28 DAF, seed 35 & 42 DAF, stably expressed
nutrient reservoir activity & urease activity. Which are imp activity in seed devt. 31
32. Genes structure and tissue specific gene expression
• Underground tissue have larger first exon, aerial has higher # of
exons.
• Significant difference in total transcription length among tissue
due to varying intron length.
• No significant difference between GC content and tissue
specificity
32
33. Boxplot Dendrogram of preferential expressed genes in seed development
RPKM normalized log2-
transformed expression
gene profiles
33
34. Summary
• RNA Seq-Atlas provides
• A record of high-resolution gene expression in a set of
14 diverse tissues
• Hierarchical clustering of transcriptional profiles for 14
tissues
• Relationship between gene structure and gene
expression
• Tissue-specific gene expression of both the most highly-
expressed genes and the genes specific to legumes in
seed development and nodule tissues
• A means of evaluating existing gene model annotations
for the Glycine max genome
34
35. Spatial transcriptome
• Most RNA-seq analyses target whole organs, or sets of
organs, which inherently prevents the identification of cell
or tissue type transcripts, and thus spatially coordinated
structural and regulatory gene networks.
• RNA-seq analysis of discrete tissues or cell types: Spatial
information and increase the depth of sequence coverage
Ex: >1000 genes have specifically or preferentially
expressed in Arabidopsis male meiocytes
• Acquiring tissue or cell-specific samples with any degree of
precision and minimal contamination is often technically
difficult
35
37. Contd…
• Matas et al. (2011): LCM + RNA-seq (454 pyrosequencing)
transcriptomes of 5 principal tissues of the developing
tomato fruit pericarp.
~ 21,000 unigenes identified & more than half showed
ubiquitous (57%) expression, while other showed cell
type-specific expression
• Takacs et al. (2012): LCM + RNA-seq (Illumina-based NGS)
study of the ontogeny of maize shoot apical meristem
59% of genes expressed ubiquitously
• A number of mammalian tissues also shown a high
proportion of ubiquitously expressed transcripts
“These studies may indicate that this is a common feature of
eukaryotes” (Ramsköld et al., 2009).
37
38. To study plant responses and adaptations to abiotic
and biotic stresses
Aim: Elucidate genes and gene networks that contribute to
sorghum’s tolerance to water-limiting environments with a long-
term aim of developing strategies to improve plant productivity
under drought
Discovered >50 previously unknown drought- responsive genes.
38
39. Up Down
ABA ~2,300 ~2,600
PEG ~1,650 ~700
20 μM
8th day,
57.1 μM
20% PEG-8000
Transcript Analysis
in Response to
ABA and Osmotic
Stress
Method
ABA in response to plant
stress, and its central role
in other pathways,
(dormancy in leaf & seed)
LEA protein
WSI18 protein
dehydrin
sugar
substrate
transporter
peroxidase 6
39
40. A, brassinosteroid biosynthesis
B, cytokinins degradation
C, cytokinins glucoside biosynthesis
D, ent-kaurene biosynthesis
E, ethylene biosynthesis from methionine
F, gibberellin biosynthesis
G, gibberellin inactivation
H, IAA conjugate biosynthesis
I, jasmonic acid
Networks of hormone pathways in ABA-treated plants
Shoots Roots
Box=Hormone-related
Circle=non-hormone-related
Down Up DE genes
Dark blue solid lines= ≥10 blue
long-dashed lines=6-9 light
blue short-dashed= ≤5
Only the brassinosteroid and JA biosynthesis pathways, and cytokinin glucoside and IAA
conjugate biosynthesis pathways are directly connected via DE genes.
Indirect ‘cross-talk’ between the various hormones in response to osmotic stress and ABA
40
41. Determining the genes of unknown
function that respond to drought or
ABA treatment across species
Decision tree used to determine which
genes and their orthologs were regulated by
drought/ABA across different species
Overlap of drought-responsive sorghum
genes of unknown function that had drought-
responsive orthologs of unknown function in
other species
41
(51) (82)
(183)
42. • RNA-seq used reveal massive changes in
metabolism and cellular physiology of the green
alga Chlamydomonas reinhardtii when the cells
become deprived of sulfur
• studies of plant responses to pathogens
Ex: sorghum Bipolaris sorghicola
(Mizunoetal.,2012)
• Complexities of the metabolic pathways associated
with plant defense mechanisms
42
43. Study plant evolution and polyploidy.
• A comparison of the leaf transcriptome of an allopolyploid relative of
soybean with two species that contributed to its homoelogous genome,
allowed the determination of the contribution of the different genomes
to the transcriptome (Ilut et al., 2012)
• Maize endosperm trascriptome analysis; discovered 179 imprinted
genes and 38 imprinted long ncRNAs (Zhang et al., 2011)
• Transcriptome of 9 distinct tissues of three species of the Poaceae
family (Brachypodium, sorghum & rice) to determine whether
orthologous genes from these three species exhibit the same expression
patterns (Davidson et al., 2012)
Only a fraction of orthologous genes exhibit conserved expression
patterns
Orthologs in syntenic genomic blocks are more likely to share
correlated expression patterns compared with non-syntenic
orthologs.
These findings are important for crop improvement (seq transfer)
43
44. Hierarchical clustering of 27 tissues (9 tissues x 3
species) based on correlations of log2 FPKM mapped
expression values of 3-taxa single-copy (3x3) genes
Classification of Brachypodium, rice, and
sorghum genes into orthologous groups
clustering of
corresponding
tissue
extensive expression divergence within 3 · 3 genes
Red: single copy (2 X 2 & 3 X 3)
Black: multicopy (2 X N & 3 x N)
OrthoMCL 44
45. Genes within each k-means co-expression
cluster were categorized based on OrthoMCL
category assignments or as lineage-specific
single-copy (1 x 1) genes.
Co-expression analyses identify conservation
of expression among orthologs and paralogs
Proportions of genes with at least one corresponding paralog or ortholog in the same cluster
Portion of Poaceae orthologs and paralogs share same
expression patterns across reproductive tissues
Some genes exhibited different expression phenotypes
45
Similar expression pattern in Poacea
46. which biological processes were over-represented in
orthologs/paralogs category ?
Ortholog/paral
ogs
Gene ontology (GO) annotation
2 x N genes Stress-related functions (‘response to biotic stimulus’, ‘defense response’,
‘apoptosis’), lipid transport, secretion (‘exocytosis’), and general
oxidation–reduction reactions.
3 x N (higher
substitution
rates)
Core metabolic functions; ‘translation, ATP biosynthesis, nucleosome
assembly, and biosynthetic process & oxidation–reduction, response to
wounding, sexual reproduction.
3 x 3 genes Essential functions: regulation of transcription’ (>1000 genes),
‘protein folding’ (253 genes), ‘intracellular protein transport’
(123 genes), and ‘glycolysis (91 genes)
2 x 2 genes protein amino acid phosphorylation, ‘regulation of transcription &
response to oxidative stress
46
47. Relationship between synteny and expression patterns of orthologs
Syntenic gene pairs within collinear blocks of at
least five genes were identified for all pairwise
combinations of three Poaceae species
Distributions of Pearson’s correlation coefficients (PCC)
synteny plays a significant role in
evolution of gene expression, especially
in the case of duplicate and multicopy
genes
47
48. Identifying and characterizing novel non-coding RNAs
• Insilico analysis provides a rapid way to identify putative sRNA
genes
• RNA-seq technology represents an excellent means for sRNA
discovery and validation
• Characterization of miRNAs regulatory functions to be facilitated
by determining tissue-specific expression pattern
• RNA-seq was used to identify sRNAs from five Arabidopsis root
tissues.
Some sRNAs expressed in all 5 tissues while others were
tissue and developmental zone specific
• The frequency of alternative slicing at miRNA binding sites is
significantly higher than that at other regions, suggesting that
alternative splicing is a significant regulatory mechanism.
• sRNAs have been recently characterized in the context of
association with epigenome modifications, including cytosine
methylation of genomic DNA
48
49. From co-expression networks to integrative data
analysis
• Sequencing whole transcriptomes provides a high degree of detail,
but deriving useful biological information from a long list of
expressed genes is typically not trivial
• Construct networks of co-expressed genes and to use gene ontology
(GO) information to help highlight important gene candidates as
critical components of functional networks
• Gene ontology enrichment analysis of RNA-seq data often illustrates
the complexity of interacting pathways
Robust Functiona
networks
Transcriptome: RNA-seq
proteomics
metabolomics
No correlation
ex: Soybean
protein X
Correlation
Ex:Oil plam
mesocarp Fatty
acid
49
50. Bulked Segregant RNA-Seq
SNP 2 being closely
related to the mutation to
map
linkage disequilibrium
between markers and
causal gene is determined
by quantifying the allelic
frequencies between two
samples
advantages:
(i) Having a reference genome is not
a prerequisite
(ii) Markers can be generated from
the experimental data
(iii) Differential expression profiles
(iv) Info on effects of mutant on
global patterns of gene
expression
(v) Provide map position of a gene
Liu et al. (2012)
BSA requires polymorphic markers
50
51. >64,000 SNPs
Two alleles of a given SNP site should be
detected in approximately equal numbers of
RNA-Seq reads when considering both pools of
RNASeq data.
Only one allele of a SNP that is completely
linked to the causal gene should be present
among the RNA-Seq reads from the mutant
pool
In practice, as a consequence of Allele Specific
Expression and sampling bias, genes expressed
at low levels, single allele of many SNPs are
detected in the mutant pool.
Empirical Bayesian approach used to estimate
linkage probability, i.e. probability of a SNP
exhibiting complete linkage disequilibrium
with the causal gene. 51
52. >64,000 SNPs
Two alleles of a given SNP site should be
detected in approximately equal numbers of
RNA-Seq reads when considering both pools of
RNASeq data.
Only one allele of a SNP that is completely
linked to the causal gene should be present
among the RNA-Seq reads from the mutant
pool
In practice, as a consequence of Allele Specific
Expression and sampling bias, genes expressed
at low levels, single allele of many SNPs are
detected in the mutant pool.
Empirical Bayesian approach used to estimate
linkage probability, i.e. probability of a SNP
exhibiting complete linkage disequilibrium
with the causal gene. 52
53. >64,000 SNPs
Two alleles of a given SNP site should be
detected in approximately equal numbers of
RNA-Seq reads when considering both pools of
RNASeq data.
Only one allele of a SNP that is completely
linked to the causal gene should be present
among the RNA-Seq reads from the mutant
pool
In practice, as a consequence of Allele Specific
Expression and sampling bias, genes expressed
at low levels, single allele of many SNPs are
detected in the mutant pool.
Empirical Bayesian approach used to estimate
linkage probability, i.e. probability of a SNP
exhibiting complete linkage disequilibrium
with the causal gene.
gl3-ref allele in a non-B73/B73
53
54. Reference genome
The top 10 windows with the highest median
linkage probability were located at physical
position ,183.5–185.2 Mb.
Fine mapping of gene.
1.Mutant gene expression will often be
down-regulated compared to the WT pool.
2. Collections of SNPs tightly linked to
mutant gene
3. SNPs linked to mutated gene can be used
for gene cloning via chromosome walking.
54
55. • Not necessary to use tissue with mutant gene
expression for BSR-Seq.
• However, if we collect tissue with expression we
can also get additional expression data.
Resolution of mapping depends on
1. # of individuals included in the bulks
2. Sequencing depth
3. Density of polymorphisms in mapping population
55
56. • International multi-disciplinary consortium; 1,000 plant sps. transcriptome data
• It is PPP project; funding of 75% from Govt. of Alberta, 25% by Musea
Ventures. BGI-Shenzhen- sequencing at reduced costs & iPlant collaborative -
computational informatics.
• Objectives:
1. Resolve many of the lingering uncertainties in species relationships,
especially in the early lineages of streptophyte green algae and land plants
2. To identify gene changes associated with the major innovations in
Viridiplantae evolution, such as multi-cellularity, transitions from marine to
freshwater or terrestrial environments, maternal retention of zygotes and
embryos, complex life history involving haploid and diploid phases, vascular
systems, seeds and flowers
• Species selection; representations of all major lineages across the Viridiplantae
(green plants), representing ~1 billion years of evolution, including flowering
plants, conifers, ferns, mosses and streptophyte green algae.
56
57. Resources available
1. Access to raw and processed data:
Content; transcriptome assemblies, putative coding
sequences, orthogroups and gene and species trees with
related sequence alignments.
2. High performance computing and cloud-based services:
iPlant discovery environment (DE) web interface (tutorials and
teaching materials available)
57
58. Phenylpropanoid synthesis pathway for Colchicum autumnale. Labelled rectangles are
proteins. Small circles are metabolites. Black lines show the KEGG pathway. Red lines show the
BioGRID interactions emanating from protein (K12355), which was interactively selected. A right-
click on the protein will display the inferred function and a link to the sequence(s)
Interactions & pathways
58
59. Conclusion
• RNA-sequencing is now well-established as a versatile
platform with applications in an ever growing number of
fields of plant biology research
• Ongoing developments in sequencing technologies, such as
increased read lengths, greater numbers of reads per run
• Advanced computational tools to facilitate sequence
assembly, analysis, and integration with orthogonal data
sets will further accelerate the breadth and frequency of its
adoption by plant scientists
59
Editor's Notes
Human genes 20,000-25,000 & rice 56,081 genes. All will not express all time and tissues.
SAGE: a tag of only 9 or 10 bases, from a specific position in the mRNA, is enough to identify the transcript. MPSS= Massively Parallel Signature Sequencing, 16-20 bp signature, CAGE=27nt 5' ends of capped transcripts
Use of close-related species is preferable, but if none is available, use A. thaliana
Gene Ontology (GO) database includes information on biological processes, molecular functions and cellular components and is an international standardized gene functional classification system which offers a dynamically updated controlled vocabulary and a strictly defined concept that describes the properties of genes and their products in any organism.
aerial and underground tissues are distinguished from seed tissue by a bimodal expression pattern with more genes from aerial and underground tissues shifted toward higher expression
Z-score help in further annotation process & investigation especially for those genes with no known annotation. Oleosin and Lectin precursor 1 gene; no expression in seed 10 DAF & high in seed 42 DAF, this warrants investigation of determine how their similar expression profiles in seed development is affected by the negative correlation between protein and oil seed content
Web interface is provided to this table, which links each table cell to a downloadable list of genes.
Underground tissue have larger first exon, aerial has higher # of exons. Significant difference in total transcription length among tissue due to varying intron length.
transcripts that are expressed at extremely low levels, or that are specific to an uncommon cell type in a complex organ or tissue, may be diluted below the limit of detection.
Overlap between differentially responsive genes following treatment with ABA and PEG. LEA proteins are water-binding molecules, membrane-stabilizers, and ion modulators induced by drought stress and ABA, a subclass of which includes dehydrins.
because sequence information is transferred from model species to crop plants without sequenced genomes
Single copy= orthologs, multicopy= paralogs The highest number of two-taxon groups (2 x 2 and 2 x N) was observed between rice and sorghum compared to Brachypodium with either rice or sorghum, consistent with genome reduction and gene loss during Brachypodium evolution.
The integration of transcriptomics, proteomics, and metabolomics can expose complex biological and biochemical interactions, paving the way to elucidate relation- ships between genotype and phenotype.
The expression patterns of genes within the mapping interval can be used to prioritize candidate genes .
In Bayesian analysis approach, a SNP is classified as having a high probability of being in complete linkage disequilibrium with the causal gene only if it is
‘‘fixed’’ in the mutant pool. If mis-inclusion of mutant in nonmutant or vice versa also it takes care.