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.
Bioo Scientific - Absolute Quantitation for RNA-SeqBioo Scientific
Accurate quantitation is a critical issue for most RNA-Seq analysis. As the efficiency of PCR amplification is sequence-dependent, with some transcripts being preferentially amplified over others, PCR amplification introduces bias during NGS library construction. The traditional approach to removing these artifacts, introduced during PCR, involves removing all fragments with identical start and stop sites. However, detailed analyses have shown that many original fragments have identical start and stop sites, and this method can incorrectly eliminate unique fragments from NGS data, thus reducing its accuracy. Bioo Scientific has incorporated Molecular Indexes™ into its NEXTflex™ Rapid Directional qRNA-Seq™ Kit, allowing for a more accurate resolution of duplicates introduced during PCR amplification. This presentation describes how Molecular Indexes can be used to increase the accuracy of RNA-Seq analysis.
Bioo Scientific - Simplify and Reduce Cost of mtDNA Isolation and Library PrepBioo Scientific
Mutations in mitochondrial DNA (mtDNA) have been implicated in various human disorders and in aging, making NGS analysis of mtDNA a priority for a number of labs. However, accurately determining the diversity of mtDNA has been difficult for a number of reasons. The standard methods for mitochondrial DNA extraction have a number of limitations making them inferior solutions for NGS library preparation. Bioo Scientific has commercialized a kit which overcomes these limitations of mtDNA isolation by selectively digesting linear nuclear DNA (nDNA) while leaving circular mtDNA intact. This technology has been incorporated into the NEXTflex mtDNA-Seq Kit which includes optimized reagents for the isolation of mtDNA and for the construction of Illumina mtDNA libraries. Libraries constructed using the NEXTflex mtDNA-Seq Kit are ideal for many NGS applications including heteroplasmy analysis.
Transcriptomics is the study of RNA, single-stranded nucleic acid, which was not separated from the DNA world until the central dogma was formulated by Francis Crick in 1958, i.e., the idea that genetic information is transcribed from DNA to RNA and then translated from RNA into protein.
Bioo Scientific - Absolute Quantitation for RNA-SeqBioo Scientific
Accurate quantitation is a critical issue for most RNA-Seq analysis. As the efficiency of PCR amplification is sequence-dependent, with some transcripts being preferentially amplified over others, PCR amplification introduces bias during NGS library construction. The traditional approach to removing these artifacts, introduced during PCR, involves removing all fragments with identical start and stop sites. However, detailed analyses have shown that many original fragments have identical start and stop sites, and this method can incorrectly eliminate unique fragments from NGS data, thus reducing its accuracy. Bioo Scientific has incorporated Molecular Indexes™ into its NEXTflex™ Rapid Directional qRNA-Seq™ Kit, allowing for a more accurate resolution of duplicates introduced during PCR amplification. This presentation describes how Molecular Indexes can be used to increase the accuracy of RNA-Seq analysis.
Bioo Scientific - Simplify and Reduce Cost of mtDNA Isolation and Library PrepBioo Scientific
Mutations in mitochondrial DNA (mtDNA) have been implicated in various human disorders and in aging, making NGS analysis of mtDNA a priority for a number of labs. However, accurately determining the diversity of mtDNA has been difficult for a number of reasons. The standard methods for mitochondrial DNA extraction have a number of limitations making them inferior solutions for NGS library preparation. Bioo Scientific has commercialized a kit which overcomes these limitations of mtDNA isolation by selectively digesting linear nuclear DNA (nDNA) while leaving circular mtDNA intact. This technology has been incorporated into the NEXTflex mtDNA-Seq Kit which includes optimized reagents for the isolation of mtDNA and for the construction of Illumina mtDNA libraries. Libraries constructed using the NEXTflex mtDNA-Seq Kit are ideal for many NGS applications including heteroplasmy analysis.
Transcriptomics is the study of RNA, single-stranded nucleic acid, which was not separated from the DNA world until the central dogma was formulated by Francis Crick in 1958, i.e., the idea that genetic information is transcribed from DNA to RNA and then translated from RNA into protein.
Next generation-sequencing.ppt-convertedShweta Tiwari
The advance version, sequences the whole genome efficiently with high speed and high throughput sequencing at reduce cost is termed as Next Generation Sequencing (NGS) or massively parallel sequencing (MPS).
TYPES OF MOLECULAR MARKERS,ITS ADVANTAGES AND DISADVANTAGESANFAS KT
Types of molecular markers (genetics)
ITS ADVANTAGES AND DISADVANTAGES
What is a genetic marker?
RFLP: Restriction fragment length polymorphism
AFLP: Amplified fragment length polymorphism
RAPD: Random amplification of polymorphic DNA
ISSR: Inter simple sequence repeat
STR: Short tandem repeats
SCAR: Sequence characterized amplified region
SNP: Single nucleotide polymorphism
SSR: Simple sequence repeat
Single Nucleotide Polymorphism Genotyping Using Kompetitive Allele Specific ...MANGLAM ARYA
Single Nucleotide Polymorphism
Single nucleotide polymorphism (SNP) refers to a single base change in a DNA sequence
SNP: Commonly biallelic
Two types(Based on presence in genome)
Synonymus
Non-synonymus
SNPs have largely replaced simple sequence repeats (SSRs)
Advantage of using SNPs
Low assay cost
High genomic abundance
Locus specificity
co-dominant inheritance
Simple documentation
Potential for high-throughput Analysis
Relatively low genotyping error rates
SNP genotyping platforms
BeadXpressTM,GoldenGateTM and Infinium from Illumina
GeneChipTM and GenFlexTM Tag array from Affimetrix
SNaPshotTM and TaqManTM from the Applied Biosystems
SNPWaveTM from KeyGene
iPLEX GoldTM Assay and Mass-RRAYTM from Sequonome
Variables to be considered
Throughput
Data turnaround
Time
Ease of use
Performance (sensitivity, reliability, reproducibility, and accuracy),
Flexibility (genotyping few samples with many snps or many samples with few snps),
Number of markers generated per run (uniplex versus multiplex assay capability)
Assay development requirements and genotyping cost per sample or data point.
KASP
KBioscience Competitive Allele-Specific PCR
Homogenous, Fluorescence-based genotyping technology, based on
Allele-specific oligo extension (primer)
Fluorescence resonance energy transfer
KASP Applications
Genotyping a wide range of species for various purposes.
KASP for Quality analysis, QTL mapping, MARS, and allele mining
Quality Control Analysis
QC analysis should be done for two reasons by genotyping the parents and F1s with the same subset of SNPs, in order to
confirm if F1s contains true-to-type alleles from their parents
check the genetic purity of the inbred parents.
F1s with true-to-type parental alleles for at least 90 % of the SNPs that were polymorphic between the parents should be advanced, while those with less than 10 % nonparental alleles should be discarded.
QTL Mapping
QTL mapping identifies a subset of markers that are significantly associated with one or more QTL influencing the expression of the trait of interest.
1) Select or develop a bi-parental mapping population.
2) Phenotype the population for a trait under greenhouse or field conditions.
3) Choose a molecular marking system – genotype parents of the mapping population and F1s with large numbers of markers, then select 200-400 markers exhibiting polymorphism between the parents.
4) Choose a genotyping approach, then generate molecular data for polymorphic markers
5) Identify the molecular markers associated with major QTL using statistical programs.
Large-scale allele mining
Allele mining is a promising approach to dissecting naturally occurring allelic variation at candidate genes controlling key agronomic traits.
KASP platform at CIMMYT has been used for the systematic mining of large germplasm collections for specific functional polymorphisms.
SNPs or small indels that
Bioo Scientific - Improving NGS Library Prep Automation on the Sciclone NGS W...Bioo Scientific
While the use of the Sciclone NGS Workstation simplifies library prep by offering increased throughput and uniformity and decreased costs, it can also be a limiting factor if the latest library prep technology is not incorporated into the kits which are automated on the platform. Bioo Scientific has developed automation protocols for the Sciclone NGS and NGSx for a number of the NEXTflex library prep kits, so that the latest innovations available to reduce bias and increase library quality are now easily available for use with the Sciclone. With automation protocols and user guides available, these rapid and robust library prep solutions also are easy to deploy and greatly increase the end user’s multiplexing capabilities.
Bioo Scientific - Improving the Performance of SureSelectXT2 Target CaptureBioo Scientific
Agilent’s SureSelectXT2 baits are popular options for target capture because they offer offer a wide range of predesigned baits and flexible customization options which allow users to design their own capture panels. Incorporating index-specific barcode blockers during library prep allow researchers to obtain a higher percentage of on-target reads and better coverage from their SureSelectXT2 target capture experiments. The NEXTflex™ Pre- and Post- Capture Combo Kit (Agilent SureSelectXT2 Compatible) incorporates index-specific barcode blockers allowing researchers to get more useful data from their Agilent SureSelectXT2 Target Capture sequencing runs.
Next generation-sequencing.ppt-convertedShweta Tiwari
The advance version, sequences the whole genome efficiently with high speed and high throughput sequencing at reduce cost is termed as Next Generation Sequencing (NGS) or massively parallel sequencing (MPS).
TYPES OF MOLECULAR MARKERS,ITS ADVANTAGES AND DISADVANTAGESANFAS KT
Types of molecular markers (genetics)
ITS ADVANTAGES AND DISADVANTAGES
What is a genetic marker?
RFLP: Restriction fragment length polymorphism
AFLP: Amplified fragment length polymorphism
RAPD: Random amplification of polymorphic DNA
ISSR: Inter simple sequence repeat
STR: Short tandem repeats
SCAR: Sequence characterized amplified region
SNP: Single nucleotide polymorphism
SSR: Simple sequence repeat
Single Nucleotide Polymorphism Genotyping Using Kompetitive Allele Specific ...MANGLAM ARYA
Single Nucleotide Polymorphism
Single nucleotide polymorphism (SNP) refers to a single base change in a DNA sequence
SNP: Commonly biallelic
Two types(Based on presence in genome)
Synonymus
Non-synonymus
SNPs have largely replaced simple sequence repeats (SSRs)
Advantage of using SNPs
Low assay cost
High genomic abundance
Locus specificity
co-dominant inheritance
Simple documentation
Potential for high-throughput Analysis
Relatively low genotyping error rates
SNP genotyping platforms
BeadXpressTM,GoldenGateTM and Infinium from Illumina
GeneChipTM and GenFlexTM Tag array from Affimetrix
SNaPshotTM and TaqManTM from the Applied Biosystems
SNPWaveTM from KeyGene
iPLEX GoldTM Assay and Mass-RRAYTM from Sequonome
Variables to be considered
Throughput
Data turnaround
Time
Ease of use
Performance (sensitivity, reliability, reproducibility, and accuracy),
Flexibility (genotyping few samples with many snps or many samples with few snps),
Number of markers generated per run (uniplex versus multiplex assay capability)
Assay development requirements and genotyping cost per sample or data point.
KASP
KBioscience Competitive Allele-Specific PCR
Homogenous, Fluorescence-based genotyping technology, based on
Allele-specific oligo extension (primer)
Fluorescence resonance energy transfer
KASP Applications
Genotyping a wide range of species for various purposes.
KASP for Quality analysis, QTL mapping, MARS, and allele mining
Quality Control Analysis
QC analysis should be done for two reasons by genotyping the parents and F1s with the same subset of SNPs, in order to
confirm if F1s contains true-to-type alleles from their parents
check the genetic purity of the inbred parents.
F1s with true-to-type parental alleles for at least 90 % of the SNPs that were polymorphic between the parents should be advanced, while those with less than 10 % nonparental alleles should be discarded.
QTL Mapping
QTL mapping identifies a subset of markers that are significantly associated with one or more QTL influencing the expression of the trait of interest.
1) Select or develop a bi-parental mapping population.
2) Phenotype the population for a trait under greenhouse or field conditions.
3) Choose a molecular marking system – genotype parents of the mapping population and F1s with large numbers of markers, then select 200-400 markers exhibiting polymorphism between the parents.
4) Choose a genotyping approach, then generate molecular data for polymorphic markers
5) Identify the molecular markers associated with major QTL using statistical programs.
Large-scale allele mining
Allele mining is a promising approach to dissecting naturally occurring allelic variation at candidate genes controlling key agronomic traits.
KASP platform at CIMMYT has been used for the systematic mining of large germplasm collections for specific functional polymorphisms.
SNPs or small indels that
Bioo Scientific - Improving NGS Library Prep Automation on the Sciclone NGS W...Bioo Scientific
While the use of the Sciclone NGS Workstation simplifies library prep by offering increased throughput and uniformity and decreased costs, it can also be a limiting factor if the latest library prep technology is not incorporated into the kits which are automated on the platform. Bioo Scientific has developed automation protocols for the Sciclone NGS and NGSx for a number of the NEXTflex library prep kits, so that the latest innovations available to reduce bias and increase library quality are now easily available for use with the Sciclone. With automation protocols and user guides available, these rapid and robust library prep solutions also are easy to deploy and greatly increase the end user’s multiplexing capabilities.
Bioo Scientific - Improving the Performance of SureSelectXT2 Target CaptureBioo Scientific
Agilent’s SureSelectXT2 baits are popular options for target capture because they offer offer a wide range of predesigned baits and flexible customization options which allow users to design their own capture panels. Incorporating index-specific barcode blockers during library prep allow researchers to obtain a higher percentage of on-target reads and better coverage from their SureSelectXT2 target capture experiments. The NEXTflex™ Pre- and Post- Capture Combo Kit (Agilent SureSelectXT2 Compatible) incorporates index-specific barcode blockers allowing researchers to get more useful data from their Agilent SureSelectXT2 Target Capture sequencing runs.
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
Proteintech: The Benchmark in Antibodies.
Learn more about Mitochondrial research, including:
- Mitochondrial markers
- The citric acid cycle
- Mitochondrial Respiratory Complexes
- Mitochondrial Fission & Fusion
and more...
How To Optimize Your Immunohistochemistry ExperimentProteintech Group
Immunohistochemistry allows the visualization of proteins in tissue while retaining its microstructure. This guide includes general protocols, technical tips and troubleshooting.
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.
A biochemical test that detects and measures antibodies
in your blood and antibodies related to certain infectious
conditions. ELISA tests are mainly used in immunology
RNAi is a highly specific post-transcriptional gene silencing process, a powerful tool for functional genomics. This guide includes protocol reviews, handy tips and troubleshooting help.
Enabling RNA-Seq With Limited RNA Using Whole Transcriptome AmplificationQIAGEN
RNA-Seq was developed to perform transcriptome profiling and provides a highly precise measurement of expression levels of transcripts and their isoforms. Normally, RNA-Seq analysis requires at least 500 ng –1 μg of total RNA. When working with small biopsies, single cells (such as circulating tumor cells), or other limited material, whole transcriptome amplification (WTA) is normally required. Various WTA methods overcome limited RNA availability and enable transcriptome analysis from limited material or even single cells. In standard PCR-based WTA procedures, however, bias from uneven coverage of cDNA regions with high GC or AT content or amplification errors can lead to the loss of transcripts and wrong variant calling. Here, we compare a standard RNA-Seq library preparation method and the REPLI-g RNA library protocol. The REPLI-g procedure is a PCR-free protocol to efficiently generate RNA-Seq libraries from small amounts of RNA or a single cell in 6.5–7 hours. The REPLI-g protocol uses whole transcriptome amplification based on multiple displacement amplification (MDA), combined with an efficient library adaptor ligation procedure, to prepare RNA-Seq libraries from small RNA amounts. The procedure demonstrates high fidelity, minimal bias and retention of sample‘s transcriptional profile. Compared to standard RNA-Seq library prep, the REPLI-g protocol demonstrates similar reproducibility and sensitivity in transcript detection.
CD Genomics provides a fast, one-stop bacterial RNA sequencing solution from the quality control of sample to comprehensive data analysis. Please contact us for more information and a detailed quote.
This pdf is about the DNA Libraries / Genomic DNA vs cDNA.
For more details visit on YouTube; @SELF-EXPLANATORY; https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos Thanks...!
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
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.
3. Substantial bias introduced during ligation steps
Need to gel purify final library
Lack of low-input protocols
PROBLEM #
1
PROBLEM #
2
PROBLEM #
3
4. PROBLEM #
1
Research shows extensive bias is typically
seen in small RNA sequencing
Baran-Gale, J., Kurtz, L. C., Erdos, Sison, M. C., Young, A., Fannin, E. E., Chines, P. S. and Sethupathy, P. (2015)
Addressing bias in small RNA library preparation for sequencing: a new protocol recovers microRNAs that evade
capture by current methods. Frontiers in Genetics. doi: 10.3389/fgene.2015.00352.
6. Research shows these inconsistencies are
primarily caused by bias introduced during
the ligation step of library prep
Jayaprakash, A. D., Jabado O., Brown, B. D. and Sachidanandam, R. (Sept 2, 2011), Identification and remediation of
biases in the activity of RNA ligases in small-RNA deep sequencing Nuc Acid Res, 1–12. doi:10.1093/nar/gkr693
Zhang, et al. (2013) High-efficiency cloning enables accurate quantification of miRNA expression by deep sequencing.
Genome Biology 14 R109.
Sun, G. (2011) A bias-reducing strategy in profiling small RNAs using Solexa. RNA. 17: 2256-2262
Sorefan, K. et al. (2012) Reducing sequencing bias of small RNAs. Silence. doi:10.1186/1758-907X-3-4
8. Published research shows randomized
adapters reduce ligation bias
Jayaprakash, A. D., Jabado O., Brown, B. D. and Sachidanandam, R. (Sept 2, 2011),
Identification and remediation of biases in the activity of RNA ligases in small-RNA
deep sequencing Nuc Acid Res, 1–12. doi:10.1093/nar/gkr693.
9. Bioo Scientific makes the only small RNA
sequencing kits for Illumina platforms that
use randomized adapters to reduce ligase bias
10. The NEXTflex Small RNA-Seq Kit v3
shows more equal coverage of an equimolar
pool of 24 miRNAs (miRNA calibrator),
demonstrating more accurate representation
of the original sample
11. Figure 1. Sequencing results from small RNA libraries created in triplicate from 1 ng of miRNA
Calibrator, an equimolar mixture of 24 miRNAs. Values farther from 1 indicate more bias.
100
10
1
0.1
0.01
0.001
Observed/Expected
miRNA Calibrator
hsa-miR-92b-5p
hsa-miR-324-3p
dme-miR-6-3p
dme-miR-4-3p
has-miR-134
has-miR-23a-5p
hsa-miR-133a
hsa-miR-127-5p
hsa-miR-24-3p
hsa-let-7e-3p
hsa-miR-195-3p
hsa-miR-92a-3p
hsa-miR-34c-3p
hsa-miR-30c-1-3p
hsa-miR-320a
hsa-miR-218-1-3p
hsa-miR-34a-5p
hsa-miR-106b-5p
hsa-miR-141-3p
hsa-let-7c
hsa-miR-15a-5p
hsa-miR-21-5p
hsa-miR-29b-3p
hsa-miR-190a
CV
NEXTflex = 1.09
Illumina = 2.35
NEB = 2.31
NEXTflex Illumina NEB
12. The NEXTflex Small RNA-Seq Kit v3
shows more even coverage with the Miltenyi
miRXplore Universal References, an
equimolar mixture of 963 miRNAs
13. Figure 2. Sequencing results from small RNA libraries created in triplicate from 1 ng of
Miltenyi miRXplore Universal Reference, an equimolar mixture of 963 miRNAs.
1000
900
800
700
600
500
400
300
200
100
0
0
miRNAsdetected
miRNAs detected
Threshold (reads/100K)
NEXTflex Illumina NEB
10 20 30 40 50 60 70 80 90 100
CV
NEXTflex = 1.14
Illumina = 3.78
NEB = 1.67
14. The use of randomized adapters in the
NEXTflex Small RNA-Seq Kit v3 greatly
improves accuracy of data by reducing
bias in small RNA library prep
CONCLUSION #
1
15. The NEXTflex Small RNA-Seq Kit v3
allows detection of more miRNAs in
total RNA samples
16. Figure 3. Small RNA libraries were created in duplicate from human brain total RNA and
sequenced on an Illumina MiSeq. The indicated number of reads was sampled from each library
and the average number of miRNA groups with ≥20 reads determined. The inset shows the
number of reads required to detect 100 miRNA groups at a threshold of ≥20 reads.
Sequencing depth versus miRNAs detected
mirRNAgroupswith≥20reads
Reads sampled
180
160
140
120
100
80
60
40
20
0
0 50000 100000 150000 200000
NEXTflex - 100 ng NEXTflex - 10 ng NEB - 100 ng Illumina - 100 ng
17. The NEXTflex Small RNA-Seq Kit v3
detects the same number of miRNAs
with fewer reads
18. Figure 4. 3 - 4.5x fewer reads are necessary to detect 100 miRNAs with
the NEXTflex Small RNA Seq-Kit v3.
Reads necessary to
detect 100 miRNAs
200000
150000
100000
50000
0
NEXTflex - 100 ng NEXTflex - 10 ng NEB - 100 ng Illumina - 100 ng
19. Reduced bias small RNA library prep
using the NEXTflex Small RNA-Seq Kit
v3 allows detection of more small RNAs
at lower sequencing depth
CONCLUSION #
2
20. PROBLEM #
2
Small RNA library preparation has historically
required PAGE gel purification due to the
presence of adapter-dimer products
24. The NEXTflex Small RNA-Seq Kit v3 uses
a dual approach to substantially reduce
adapter-dimer formation, allowing gel-free
purification of final libraries
25. Gel-free libraries prepared with the
NEXTflex Small RNA-Seq Kit v3 have
a higher proportion of reads mapping
to miRNAs
26. Figure 5. Percent of total reads aligned to miRBase in gel-free libraries
created from 100 ng human brain total RNA input.
0
10
20
30
40
50
60
70
Bioo-100 ng Bioo- 10 ng
%miRBasealigned
Overall alignment rate% miRBase alignment
NEXTflex NEB
70
60
50
40
30
20
10
0
32. PCR bias adds negligible bias to
small RNA libraries
33. Correlation of miRNA expression
demonstrates negligible bias added by
additional PCR cycles
PCR Cycles 12 16 20 24 28
12 1 0.9999 0.9994 0.9966 0.9807
16 1 0.9996 0.9970 0.9813
20 1 0.9975 0.9824
24 1 0.9816
28 1
Table 1. Correlation of miRNA abundance in libraries created using serial 10x dilutions
of cDNA and the indicated number of PCR cycles. The Pearson correlation coefficients
calculated from the Log10(reads) values of miRNAs with ≥ 10 reads in all samples are shown
34. Published data shows that additional
PCR cycles add negligible bias
to small RNA libraries
Jayaprakash, A.D., et al., Identification and remediation of biases in the activity of
RNA ligases in small-RNA deep sequencing. Nucleic Acids Res, 2011. 39(21): p. e141.
Hafner, M., et al., RNA-ligase-dependent biases in miRNA representation in deep-
sequenced small RNA cDNA libraries. RNA, 2011. 17(9): p. 1697-712.
35. Expression values are reproducible across
different sample inputs
Figure 6. Correlation of miRNA expression between samples created with 100 ng and 10 ng
of human brain total RNA with the NEXTflex Small RNA-Seq kit v3. The Pearson correlation
coefficient is shown.
1
1.5
2
2.5
3
3.5
4
4.5
5
1 1.5 2 2.5 3 3.5 4 4.5 5
Log10(reads)100ng
Log10(reads) 10 ng
100 ng vs 10 ng100 ng vs 10 ngLog10(reads)100ng
Log10(reads) 100 ng
5
4.5
4
3.5
3
2.5
2
1.5
1
1 1.5 2 2.5 3 3.5 4 4.5 5
R = 0.964
37. Enhanced reduction of adapter-dimer
formation allows library preparation
from as little as 1 ng of total RNA
CONCLUSION #
4
38. The NEXTflex Small RNA-Seq Kit v3 is the
only commercially available kit that includes
randomized adapters to reduce bias and
allows gel-free or low-input library prep
39. NEXTflex™ Small RNA-Seq Kit v3
(Illumina® Compatible)
• 48 unique barcodes for multiplexing included with 48 reaction kit
• 8 reaction kit includes barcodes that allow low-level multiplexing