Part 6 of the training sesson 'RNA-seq for differential expression analysis' considers gene set analysis for inferring biology from RNA-seq data. See http://www.bits.vib.be
Part 6 of RNA-seq for DE analysis: Detecting biology from differential expres...Joachim Jacob
Sixth part of the training session 'RNA-seq for Differential expression analysis'. We explain how we extract biological meaningful results from differential expression analysis results, based on RNA-seq. Interested in following this session? Please contact http://www.jakonix.be/contact.html
RNA-seq for DE analysis: extracting counts and QC - part 4BITS
Part 4 of the training sesson 'RNA-seq for differential expression analysis' considers extracting the count table from a mapping, and performing QC to detect sample biases. See http://www.bits.vib.be
RNA-seq for DE analysis: detecting differential expression - part 5BITS
Part 5 of the training sesson 'RNA-seq for differential expression analysis' considers the algorithm used for detecting differential expression between conditions. See http://www.bits.vib.be
Part 5 of RNA-seq for DE analysis: Detecting differential expressionJoachim Jacob
Fifth part of the training session 'RNA-seq for Differential expression analysis'. We explain the most important concepts of detecting DE expression based on a count table, explaining DESeq2 algorithm. Interested in following this session? Please contact http://www.jakonix.be/contact.html
Part 4 of RNA-seq for DE analysis: Extracting count table and QCJoachim Jacob
Fourth part of the training session 'RNA-seq for Differential expression analysis'. We explain how we get a count table from a mapping result. We show how to do quality control on the count table. Interested in following this session? Please contact http://www.jakonix.be/contact.html
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
Part 6 of RNA-seq for DE analysis: Detecting biology from differential expres...Joachim Jacob
Sixth part of the training session 'RNA-seq for Differential expression analysis'. We explain how we extract biological meaningful results from differential expression analysis results, based on RNA-seq. Interested in following this session? Please contact http://www.jakonix.be/contact.html
RNA-seq for DE analysis: extracting counts and QC - part 4BITS
Part 4 of the training sesson 'RNA-seq for differential expression analysis' considers extracting the count table from a mapping, and performing QC to detect sample biases. See http://www.bits.vib.be
RNA-seq for DE analysis: detecting differential expression - part 5BITS
Part 5 of the training sesson 'RNA-seq for differential expression analysis' considers the algorithm used for detecting differential expression between conditions. See http://www.bits.vib.be
Part 5 of RNA-seq for DE analysis: Detecting differential expressionJoachim Jacob
Fifth part of the training session 'RNA-seq for Differential expression analysis'. We explain the most important concepts of detecting DE expression based on a count table, explaining DESeq2 algorithm. Interested in following this session? Please contact http://www.jakonix.be/contact.html
Part 4 of RNA-seq for DE analysis: Extracting count table and QCJoachim Jacob
Fourth part of the training session 'RNA-seq for Differential expression analysis'. We explain how we get a count table from a mapping result. We show how to do quality control on the count table. Interested in following this session? Please contact http://www.jakonix.be/contact.html
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
Part 2 of RNA-seq for DE analysis: Investigating raw dataJoachim Jacob
Second part of the training session 'RNA-seq for Differential expression' analysis. We explain the characteristics of RNA-seq data that allow us to detect differential expression. Interested in following this session? Please contact http://www.jakonix.be/contact.html
This is the last presentation of the BITS training on 'Comparative genomics'.
It reviews tthe Contra tool for detecting common transcription factor binding sites in sequences.
Thanks to Stefan Broos of the DMBR department of VIB
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.
This session will follow up from transcript quantification of RNAseq data and discusses statistical means of identifying differentially regulated transcripts, and isoforms and contrasts these against microarray analysis approaches.
Abstract: The focus in this session will be put on the differences between standard DNA mapping and RNAseq-specific transcript mapping: identifying splice variants and isoforms. The issue of transcript quantification and genomic variants that can be identified from RNAseq data will be discussed.
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
Apollo is a web-based application that supports and enables collaborative genome curation in real time, allowing teams of curators to improve on existing automated gene models through an intuitive interface. Apollo allows researchers to break down large amounts of data into manageable portions to mobilize groups of researchers with shared interests.
An introduction on gene annotation & curation for the IAGC and BIPAA research communities.
This 1st presentation in the training "Introduction to linux for bioinformatics" gives an introduction to Linux, and the concepts by which Linux operates.
Part 2 of RNA-seq for DE analysis: Investigating raw dataJoachim Jacob
Second part of the training session 'RNA-seq for Differential expression' analysis. We explain the characteristics of RNA-seq data that allow us to detect differential expression. Interested in following this session? Please contact http://www.jakonix.be/contact.html
This is the last presentation of the BITS training on 'Comparative genomics'.
It reviews tthe Contra tool for detecting common transcription factor binding sites in sequences.
Thanks to Stefan Broos of the DMBR department of VIB
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.
This session will follow up from transcript quantification of RNAseq data and discusses statistical means of identifying differentially regulated transcripts, and isoforms and contrasts these against microarray analysis approaches.
Abstract: The focus in this session will be put on the differences between standard DNA mapping and RNAseq-specific transcript mapping: identifying splice variants and isoforms. The issue of transcript quantification and genomic variants that can be identified from RNAseq data will be discussed.
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
Apollo is a web-based application that supports and enables collaborative genome curation in real time, allowing teams of curators to improve on existing automated gene models through an intuitive interface. Apollo allows researchers to break down large amounts of data into manageable portions to mobilize groups of researchers with shared interests.
An introduction on gene annotation & curation for the IAGC and BIPAA research communities.
This 1st presentation in the training "Introduction to linux for bioinformatics" gives an introduction to Linux, and the concepts by which Linux operates.
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
Presentation given at the Stockholm R useR Group (SRUG) meetup on Dec 6, 2016. Contains a general overview of deep learning, material on using Tensorflow in R etc.
BITS - Comparative genomics on the genome levelBITS
This is the third presentation of the BITS training on 'Comparative genomics'.
It reviews the basic concepts of sequence homology on the gene
Thanks to Klaas Vandepoele of the PSB department.
The structure of Linux - Introduction to Linux for bioinformaticsBITS
This 3th slide deck of the training 'Introduction to linux for bioinformatics' gives a broad overview of the file system structure of linux. We very gently introducte the command line in this presentation.
Presentation by Valerie Schneider discussing Genome Reference Consortium (GRC) plans for the mouse and zebrafish reference genome assemblies, presented at the 2016 meeting of the The Allied Genetic Conference (TAGC). Includes description of resources at the National Center for Biotechnology Information (NCBI) for working with reference genome assemblies.
This is the third presentation of the BITS training on 'Mass spec data processing'.
It reviews the methods for matching mass spectrometry data with protein sequences, with review of useful tools.
Thanks to the Compomics Lab of the VIB for contribution.
How to do successful gene expression analysis - Siena 20100625Biogazelle
Despite its conceptual and practical simplicity, qPCR based expression analysis involves multiple steps, all of which need to be perfect in order to obtain reliable results in the end. This presentation describes points of attention, potential pitfalls and suggestions for improvements on every step along the workflow. By implementing these guidelines in your experiments you increase the chance of doing successful gene expression analysis.
Microarray data and pathway analysis: example from the benchMaté Ongenaert
Microarray data and pathway analysis: example from the bench
by drs. Jolien Vermeire - HIVlab, Department of Clinical Chemistry, Microbiology and Immunology – UGent
The increased availability and lower cost of gene expression microarrays has stimulated the use of transcriptome studies in a high variety of fields. Generating expression data at whole-genome level can indeed be a powerful method to characterize cellular pathways involved in a certain biological process. However, the challenge of extracting relevant biological information from such large datasets still prevents researchers from exploiting this tool. In this presentation I will share my personal experience, as a 'researcher non-bioinformatician', with performing microarray data and pathway analyses. I will give a general overview of the different steps that where followed in order to transform raw gene expression data, obtained in context of HIV research, into useful biological information and highlight different methods and software tools that helped me in this process.
This is the webinar presented on the 14th April as part of the Ensembl Online Webinar series. You can view the recorded webinar on the Ensembl Helpdesk youtube channel https://www.youtube.com/watch?v=blbhuqiiDoA
1
Phylogenetic Analysis Homework assignment
This assignment will be completed on your own and turned in the week of 11/8-11/10.
Introduction
Molecular evolution is the study of how proteins and nucleic acids evolve. Included in this
field are studies of mutations and chromosomal rearrangements, the evolutionary process,
the identification of sequence patterns conferring function in proteins and nucleic acids,
and the reconstruction of the evolutionary history of organisms and the molecules that
they make. All of these studies rely on comparisons of nucleotide or amino acid sequences.
In this tutorial, you will be introduced to some of the fundamental principles of molecular
evolution and the types of bioinformatics tools that are used in evolutionary studies. We
will begin by carrying out a manual sequence comparison, so that the basic concepts can
be introduced, and the remainder of the project will be carried out at The Biology
Workbench, a set of bioinformatics analysis programs managed by The San Diego
Supercomputing Center at the University of California, San Diego.
Objectives
• To introduce the principles of molecular evolution
• To acquaint you with the tools that are available to compare nucleotide and
amino acid sequences
• To learn about the use of protein sequences in reconstructions of evolutionary history
Project
Branching evolution occurs when one ancestral species gives rise to two or more progeny
species. However, speciation events don't involve the vast majority of the genes in a
genome. That is, for most genes, both of the progeny species inherit identical genes from
the ancestor. Following speciation, these genes evolve independently in the separate
lineages. Studies of molecular evolution therefore rely heavily on comparisons of related
sequences from different organisms.
Shown below is an alignment of two homologous sequences that we will use as a starting
place. Homologous sequences are sequences that have descended from a common
ancestral sequence. You can't meaningfully compare sequences unless they are
homologous. This alignment uses the single letter amino acid code, in which G represents
glycine, Q represents glutamine, etc. The aligned proteins have been shown to be involved
in the metabolism of similar, but different, toxic compounds. As you can see, these amino
acid sequences are very similar and it is easy to recognize that they are related by common
descent.
2
dntAc: KMGVDDEVIVSRQNDGSVR
nahAc: KMGIDDEVIVSRQSDGSIR
An expanded version of this alignment is shown below. In this expanded alignment, both
the amino acids and the corresponding DNA nucleotides are shown. For ease of analysis,
the codons have been broken into separate entries in a table.
Alignment of nahAc and dntAc sequences.
K M G V D E V I V
dntAc AAA ATG GGC GTC GAT GAA GTC ATC GTC
nahAc ...
Analysis of gene expression microarray data of patients with Spinal Muscular ...Anton Yuryev
By examining experimental gene expression data researchers can identify potential upstream regulatory factors that may control key biological processes. In this paper we examine the effectiveness of two similar approaches to this type of identification using a publicly available data set from research done on Spinal Muscular Atrophy.
One-way analysis of variance (ANOVA) tests allow you to determine if one given factor, such as drug treatment,
has a significant effect on gene expression behavior across any of the groups under study. A significant p-value
resulting from a 1-way ANOVA test would indicate that a gene is differentially expressed in at least one of the
groups analyzed. If there are more than two groups being analyzed, however, the 1-way ANOVA does not
specifically indicate which pair of groups exhibits statistical differences. Post Hoc tests can be applied in this
specific situation to determine which specific pair/pairs are differentially expressed. This document will provide
the necessary information for you to perform these analyses within GeneSpring.
Being able to identify genes, compare them, analyze them could be applied in various research areas from medical to industrial.
This ppt is designed for Health science and computational biology students to enable you understand the above mentioned topic.
Current trends in pseduogene detection and characterizationShreya Feliz
This presentation gives the insight of the current trends in detecting and characterizing Pseudogenes. Pseudogenes detection by bioinformatics may enhance the understanding of Pseudogenes and take research to the next step.
Using VarSeq to Improve Variant Analysis Research WorkflowsDelaina Hawkins
Many questions must be answered when analyzing DNA sequence variants: How do I determine which variants are potentially deleterious? Is the sequencing quality sufficient? How do I prioritize the results? Which annotation sources may help answer my research question?
In this webinar presentation, we will review workflow strategies for quality control and analysis of DNA sequence variants using the VarSeq software package from Golden Helix. VarSeq is a powerful platform for analysis of DNA sequence variants in clinical and translational research settings. VarSeq provides researchers with easy access to curated public databases of variant annotation information, and also enables users to incorporate their own local databases or downloaded information about variants and genomic regions.
The presentation will include interactive demonstrations using VarSeq to analyze variants found by exome sequencing of an extended family with a complex disease. We will review strategies for assessing variant quality, applying genomic annotations, incorporating custom annotation sources, and creating variant filters in VarSeq. We will also demonstrate the PhoRank gene ranking algorithm and its application for prioritizing variants.
Using VarSeq to Improve Variant Analysis Research WorkflowsGolden Helix Inc
In this webinar presentation, we will review workflow strategies for quality control and analysis of DNA sequence variants using the VarSeq software package from Golden Helix. VarSeq is a powerful platform for analysis of DNA sequence variants in clinical and translational research settings. VarSeq provides researchers with easy access to curated public databases of variant annotation information, and also enables users to incorporate their own local databases or downloaded information about variants and genomic regions.
Similar to RNA-seq for DE analysis: the biology behind observed changes - part 6 (20)
BITS - Comparative genomics: gene family analysisBITS
This is the second presentation of the BITS training on 'Comparative genomics'.
It reviews the different methods of investigating sequence homology on the gene family level.
Thanks to Klaas Vandepoele of the PSB department.
This is the first presentation of the BITS training on 'Comparative genomics'.
It reviews the basic concepts of sequence homology on different levels.
Thanks to Klaas Vandepoele of the PSB department.
BITS - Protein inference from mass spectrometry dataBITS
This is the fifth presentation of the BITS training on 'Mass spec data processing'.
It reviews the problems of determining protein sequences of mass spec data, how to deal with it, with an overview of useful tools.
Thanks to the Compomics Lab of the VIB for their contribution.
BITS - Overview of sequence databases for mass spectrometry data analysisBITS
This is the fourth presentation of the BITS training on 'Mass spec data processing'.
It review sequences databases and their flaws in light of mass spectrometry data analysis.
Thanks to the Compomics Lab of the VIB for their contribution.
This is the second presentation of the BITS training on 'Mass spec data processing'.
It reviews the methods for separating protein mixtures prior to further analysis.
Thanks to the Compomics Lab of the VIB for contribution.
BITS - Introduction to Mass Spec data generationBITS
This is the first presentation of the BITS training on 'Mass spec data processing'.
It reviews the basic concepts of mass spectrometry data generation.
Thanks to the Compomics Lab of the VIB for contribution.
These is the second part of the lecture slides of the BITS bioinformatics training session on the UCSC Genome Browser.
See http://www.bits.vib.be/index.php?option=com_content&view=article&id=17203990:orange-genome-browsers-ucsc-training&catid=81:training-pages&Itemid=190
These are the lecture slides for the BITS training session "Introduction to programming in Bioperl".
See for more material: http://www.bits.vib.be/index.php?option=com_content&view=article&id=17203793:bioperl-additional-material&catid=84&Itemid=610
This is the presentation of the BITS training session on "Essential statistics".
View more material on http://www.bits.vib.be/index.php?option=com_content&view=article&id=17203865:essential-statistics&catid=81:training-pages&Itemid=190
These are the first lecture slides of the BITS bioinformatics training session on the UCSC Genome Browser.
See http://www.bits.vib.be/index.php?option=com_content&view=article&id=17203990:orange-genome-browsers-ucsc-training&catid=81:training-pages&Itemid=190
BITS: Introduction to Linux - Software installation the graphical and the co...BITS
This slide is part of the BITS training session: "Introduction to linux for life sciences."
See http://www.bits.vib.be/index.php?option=com_content&view=article&id=17203890%3Abioperl-additional-material&catid=84&Itemid=284
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Home assignment II on Spectroscopy 2024 Answers.pdf
RNA-seq for DE analysis: the biology behind observed changes - part 6
1. The biology behind
expression differences
RNA-seq for DE analysis training
Joachim Jacob
20 and 27 January 2014
This presentation is available under the Creative Commons Attribution-ShareAlike 3.0 Unported License. Please refer to
http://www.bits.vib.be/ if you use this presentation or parts hereof.
3. Analyzing the DE analysis results
The 'detect differential
expression' tool gives you four
results: the first is the report
including graphs.
Only lower than
cut-off and with
indep filtering.
All genes, with indep
filtering applied.
Complete DESeq results,
without indep filtering
applied.
4. Analyzing the DE analysis results
Only lower than
cut-off and with
indep filtering.
All genes, with indep
filtering applied.
Complete DESeq results,
without indep filtering
applied.
5. Setting a cut-off
You choose a cut-off!
You can go over the
genes one by one, and
look for 'interesting'
genes, and try to link it
to the experimental
conditions.
Alternative: we can
take all genes, ranked
by their p-value (which
stands a 'level of
surprise'). Pro: we
don't need our
arbitrary cut-off.
6. Analysis of the list of DE genes
All genes (6666 yeast genes)
Genes sensible to test (filtered
out 10% of the lowest genes)
(5830 yeast genes)
DE genes with p-value
cut-off of 0,01 (637
genes)
7. Gene set enrichment
●
We use the knowledge already available
on biology. We construct list of genes for:
●
Pathways
●
Biological processes
●
Cellular components
●
Molecular functions
●
Transcription binding sites
●
...
http://wiki.bits.vib.be/index.php/Gene_set_enrichment_analysis
15. Artificial?
DE results
But cut-off remains artificial,
arbitrarily chosen. Rerun with
different cut-off: you will detect
other significant sets!
The background needs to be
carefully chosen.
This approach favors gene sets
with genes whose expression
differs a lot ('high level of
surprise', p-value).
17. Cut-off free approach
No cut-off needs to be chosen
using GSEA and derived
methods!
We take into account all genes
for which we get a reliable
p-value. (see the p-value
histogram chart).
The genes are sorted/ranked
according to 'level of surprise',
i.e. by their p-value. (other
options are test-statistics (T,...))
18. Intuition of GSEA
Gene set 1
Running sum:
Every occurrence
increases the sum,
every absence
decreases the sum.
The maximum is
the MES, the
final score
0
p-value
1
Mootha et al. http://www.nature.com/ng/journal/v34/n3/full/ng1180.html
19. Intuition of GSEA
Gene set 2
Higher running sum MES
Gene set 3
Median running sum MES
Low running sum MES
Gene set 4
The scores are compared to permutated/shuffled gene
set (sample label versus gene label permutation).
0
p-value
1
20. Cut-off free approach
The advantages:
● Robustness about mapping
errors influencing counts
● The set can be detected even
if some genes are not present.
● Tolerance if gene set contains
incorrect genes.
● Strong signal if all genes are
only seemingly lightly
overexpressed.
21. With cut-off applied
Genes involved in
oxidative phosphorylation
Significant DE genes
(p-value <0,05)
Mootha et al. http://www.nature.com/ng/journal/v34/n3/full/ng1180.html
22. Cut-off free approach
Genes involved in oxidative
phosphorylation are nearly
all slightly overexpressed.
This can be detected by
gene set analysis.
Mootha et al. http://www.nature.com/ng/journal/v34/n3/full/ng1180.html
23. GSEA has inspired others.
Different methods exist to rank the genes, to
calculate the running sum, and to check
significance of the running sum. In addition,
directionality of the changes can be incorporated.
Varemo et al. http://nar.oxfordjournals.org/content/early/2013/02/26/nar.gkt111
25. Piano provides a consensus output
Piano has combined
different methods and
calculates a consensus
score. It does this for 5
different types of
'directionality classes'.
The main output is a
heatmap with gene set
significantly enriched,
depleted or just changed.
The sets
Ranks! Lower is
'more important'
26. Piano provides a consensus output
1) distinct-directional down: gene set as a whole is downregulated.
2) mixed-directional down: A subset of the set is significantly downregulated
3) non-directional: the set is enriched in significant DE genes without taking
into account directionality.
4) mixed-directional up: A subset of the set is significantly upregulated
5) distinct-directional up: gene set as a whole is upregulated.