scholarly journals FrAnTK: A Frequency-based Analysis ToolKit for efficient exploration of allele sharing patterns in present-day and ancient genomic datasets

2021 ◽  
Author(s):  
J Víctor Moreno-Mayar
Keyword(s):  
Large Scale ◽  
Sequencing Data ◽  
Scale Population ◽  
Genomic Studies ◽  
Minimal Data ◽  
Efficient Exploration ◽  
Sharing Patterns ◽  
User Friendly ◽  
Fast Memory ◽  
Memory Efficient

Abstract Present-day and ancient population genomic studies from different study organisms have rapidly become accessible to diverse research groups worldwide. Unfortunately, as datasets and analyses become more complex, researchers with less computational experience often miss their chance to analyse their own data. We introduce FrAnTK, a user-friendly toolkit for computation and visualisation of allele frequency-based statistics in ancient and present-day genome variation datasets. We provide fast, memory-efficient tools that allow the user to go from sequencing data to complex exploratory analyses and visual representations with minimal data manipulation. Its simple usage and low computational requirements make FrAnTK ideal for users that are less familiar with computer programming carrying out large-scale population studies.

scholarly journals The single-cell eQTLGen consortium

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
MGP van der Wijst ◽  
DH de Vries ◽  
HE Groot ◽  
G Trynka ◽  
CC Hon ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Sequencing Data ◽  
Scale Population ◽  
Trait Locus ◽  
Different Cell Types ◽  
Affect Gene Expression

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

scholarly journals LSTrAP-Cloud: A User-Friendly Cloud Computing Pipeline to Infer Coexpression Networks

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 428 ◽  
Author(s):  
Qiao Wen Tan ◽  
William Goh ◽  
Marek Mutwil
Keyword(s):  
Rna Sequencing ◽  
Gene Function ◽  
Large Scale ◽  
Single Gene ◽  
Function Prediction ◽  
Sequencing Data ◽  
Gene Function Prediction ◽  
European Nucleotide Archive ◽  
User Friendly ◽  
Coexpression Networks

As genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, the Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes involved in a biological process that they might be interested in, by using a single gene of interest as bait. The LSTrAP-Cloud is based on Google Colaboratory, and provides user-friendly tools that process quality-control RNA sequencing data streamed from the European Nucleotide Archive. The LSTRAP-Cloud outputs a gene coexpression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway of Nicotiana tabacum as a case study to demonstrate how enzymes, transporters, and transcription factors involved in the synthesis, transport, and regulation of nicotine can be identified using our pipeline.

scholarly journals Reveel: large-scale population genotyping using low-coverage sequencing data

2015 ◽  
Vol 32 (11) ◽  
pp. 1686-1696 ◽  
Author(s):  
Lin Huang ◽  
Bo Wang ◽  
Ruitang Chen ◽  
Sivan Bercovici ◽  
Serafim Batzoglou
Keyword(s):  
Large Scale ◽  
Sequencing Data ◽  
Scale Population ◽  
Low Coverage

scholarly journals FindZX: an automated pipeline for detecting and visualising sex chromosomes using whole-genome sequencing data

2021 ◽  
Author(s):  
Hanna Sigeman ◽  
Bella Sinclair ◽  
Bengt Hansson
Keyword(s):  
Sex Chromosomes ◽  
Sex Chromosome ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
A Genome ◽  
Genomic Studies ◽  
Taxonomic Groups ◽  
User Friendly ◽  
Genomic Patterns

Sex chromosomes have evolved numerous times, as revealed by recent genomic studies. However, large gaps in our knowledge of sex chromosome diversity across the tree of life remain. Filling these gaps, through the study of novel species, is crucial for improved understanding of why and how sex chromosomes evolve. Characterization of sex chromosomes in already well-studied organisms is also important to avoid misinterpretations of population genomic patterns caused by undetected sex chromosome variation. Here we present findZX, an automated Snakemake-based computational pipeline for detecting and visualizing sex chromosomes through differences in genome coverage and heterozygosity between males and females. FindZX is user-friendly and scalable to suit different computational platforms and works with any number of male and female samples. An option to perform a genome coordinate lift-over to a reference genome of another species allows users to inspect sex- linked regions over larger contiguous chromosome regions, while also providing important between- species synteny information. To demonstrate its effectiveness, we applied findZX to publicly available genomic data from species belonging to widely different taxonomic groups (mammals, birds, reptiles, fish, and insects), with sex chromosome systems of different ages, sizes, and levels of differentiation. We also demonstrate that the lift-over method is robust over large phylogenetic distances (>80 million years of evolution).

scholarly journals LSTrAP-Cloud: A User-friendly Cloud Computing Pipeline to Infer Co-functional and Regulatory Networks

2020 ◽  
Author(s):  
Qiao Wen Tan ◽  
William Goh ◽  
Marek Mutwil
Keyword(s):  
Rna Sequencing ◽  
Gene Function ◽  
Regulatory Networks ◽  
Large Scale ◽  
Function Prediction ◽  
Sequencing Data ◽  
Gene Function Prediction ◽  
Analysis Pipeline ◽  
User Friendly

AbstractAs genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes that they might be interested in. LSTrAP-Cloud is based on Google Colaboratory and provides user-friendly tools that process and quality-control RNA sequencing data streamed from the European Sequencing Archive. LSTRAP-Cloud outputs a gene co-expression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway of Nicotiana tabacum as a case study to demonstrate how enzymes, transporters and transcription factors involved in the synthesis, transport and regulation of nicotine can be identified using our pipeline.

scholarly journals Predicting functional effect of missense variants using graph attention neural networks

2021 ◽  
Author(s):  
Haicang Zhang ◽  
Michelle S. Xu ◽  
Wendy K. Chung ◽  
Yufeng Shen
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
De Novo ◽  
Neurodevelopmental Disorder ◽  
Loss Of Function ◽  
Sequencing Data ◽  
Missense Variants ◽  
Scale Population ◽  
Scan Data ◽  
Main Component

AbstractAccurate prediction of damaging missense variants is critically important for interpretating genome sequence. While many methods have been developed, their performance has been limited. Recent progress in machine learning and availability of large-scale population genomic sequencing data provide new opportunities to significantly improve computational predictions. Here we describe gMVP, a new method based on graph attention neural networks. Its main component is a graph with nodes capturing predictive features of amino acids and edges weighted by coevolution strength, which enables effective pooling of information from local protein sequence context and functionally correlated distal positions. Evaluated by deep mutational scan data, gMVP outperforms published methods in identifying damaging variants in TP53, PTEN, BRCA1, and MSH2. Additionally, it achieves the best separation of de novo missense variants in neurodevelopmental disorder cases from the ones in controls. Finally, the model supports transfer learning to optimize gain- and loss-of-function predictions in sodium and calcium channels. In summary, we demonstrate that gMVP can improve interpretation of missense variants in clinical testing and genetic studies.

scholarly journals Genome-wide identification of directed gene networks using large-scale population genomics data

2017 ◽  
Author(s):  
René Luijk ◽  
Koen F. Dekkers ◽  
Maarten van Iterson ◽  
Wibowo Arindrarto ◽  
Annique Claringbould ◽  
...  
Keyword(s):  
Gene Networks ◽  
Large Scale ◽  
Target Genes ◽  
Population Genomics ◽  
Regulatory Gene ◽  
Sequencing Data ◽  
Scale Population ◽  
Transcriptional Changes ◽  
New Gene ◽  
Novel Target

ABSTRACTIdentification of causal drivers behind regulatory gene networks is crucial in understanding gene function. We developed a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). The analysis of genotype and whole-blood RNA-sequencing data from 3,072 individuals identified 49 genes as drivers of downstream transcriptional changes (P < 7 × 10−10), among which transcription factors were overrepresented (P = 3.3 × 10−7). Our analysis suggests new gene functions and targets including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (novel target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6,600 genes with a genetic instrument can be explored individually using a web-based browser.

scholarly journals High-quality carnivore genomes from roadkill samples enable species delimitation in aardwolf and bat-eared fox

2020 ◽  
Author(s):  
Rémi Allio ◽  
Marie-Ka Tilak ◽  
Céline Scornavacca ◽  
Nico L. Avenant ◽  
Erwan Corre ◽  
...  
Keyword(s):  
Large Scale ◽  
Incomplete Lineage Sorting ◽  
Cost Effective ◽  
Genomic Diversity ◽  
Sequencing Data ◽  
Lineage Sorting ◽  
Long Reads ◽  
Genomic Studies ◽  
Eastern And Southern Africa ◽  
Reference Genomes

AbstractIn a context of ongoing biodiversity erosion, obtaining genomic resources from wildlife is becoming essential for conservation. The thousands of yearly mammalian roadkill could potentially provide a useful source material for genomic surveys. To illustrate the potential of this underexploited resource, we used roadkill samples to sequence reference genomes and study the genomic diversity of the bat-eared fox (Otocyon megalotis) and the aardwolf (Proteles cristata) for which subspecies have been defined based on similar disjunct distributions in Eastern and Southern Africa. By developing an optimized DNA extraction protocol, we successfully obtained long reads using the Oxford Nanopore Technologies (ONT) MinION device. For the first time in mammals, we obtained two reference genomes with high contiguity and gene completeness by combining ONT long reads with Illumina short reads using hybrid assembly. Based on re-sequencing data from few other roakill samples, the comparison of the genetic differentiation between our two pairs of subspecies to that of pairs of well-defined species across Carnivora showed that the two subspecies of aardwolf might warrant species status (P. cristata and P. septentrionalis), whereas the two subspecies of bat-eared fox might not. Moreover, using these data, we conducted demographic analyses that revealed similar trajectories between Eastern and Southern populations of both species, suggesting that their population sizes have been shaped by similar environmental fluctuations. Finally, we obtained a well resolved genome-scale phylogeny for Carnivora with evidence for incomplete lineage sorting among the three main arctoid lineages. Overall, our cost-effective strategy opens the way for large-scale population genomic studies and phylogenomics of mammalian wildlife using roadkill.

scholarly journals Large-scale population analysis of SARS-CoV-2 whole genome sequences reveals host-mediated viral evolution with emergence of mutations in the viral Spike protein associated with elevated mortality rates

2020 ◽  
Author(s):  
Carlos Farkas ◽  
Andy Mella ◽  
Jody J. Haigh
Keyword(s):  
Large Scale ◽  
Population Analysis ◽  
Viral Evolution ◽  
Population Level ◽  
Spike Protein ◽  
Host Immune System ◽  
Sequencing Data ◽  
Scale Population ◽  
Significant Public Health ◽  
Viral Sequences

AbstractBackgroundWe aimed to further characterize and analyze in depth intra-host variation and founder variants of SARS-CoV-2 worldwide up until August 2020, by examining in excess of 94,000 SARS-CoV-2 viral sequences in order to understand SARS-CoV-2 variant evolution, how these variants arose and identify any increased mortality associated with these variants.Methods and FindingsWe combined worldwide sequencing data from GISAID and Sequence Read Archive (SRA) repositories and discovered SARS-CoV-2 hypermutation occurring in less than 2% of COVID19 patients, likely caused by host mechanisms involved APOBEC3G complexes and intra-host microdiversity. Most of this intra-host variation occurring in SARS-CoV-2 are predicted to change viral proteins with defined variant signatures, demonstrating that SARS-CoV-2 can be actively shaped by the host immune system to varying degrees. At the global population level, several SARS-CoV-2 proteins such as Nsp2, 3C-like proteinase, ORF3a and ORF8 are under active evolution, as evidenced by their increased πN/πS ratios per geographical region. Importantly, two emergent variants: V1176F in co-occurrence with D614G mutation in the viral Spike protein, and S477N, located in the Receptor Binding Domain (RBD) of the Spike protein, are associated with high fatality rates and are increasingly spreading throughout the world. The S477N variant arose quickly in Australia and experimental data support that this variant increases Spike protein fitness and its binding to ACE2.ConclusionsSARS-CoV-2 is evolving non-randomly, and human hosts shape emergent variants with positive fitness that can easily spread into the population. We propose that V1776F and S477N variants occurring in the Spike protein are two novel mutations occurring in SARS-CoV-2 and may pose significant public health concerns in the future.

scholarly journals CDKL5 variants

2017 ◽  
Vol 3 (6) ◽  
pp. e200 ◽  
Author(s):  
Ralph D. Hector ◽  
Vera M. Kalscheuer ◽  
Friederike Hennig ◽  
Helen Leonard ◽  
Jenny Downs ◽  
...  
Keyword(s):  
Large Scale ◽  
Catalytic Domain ◽  
Genetic Diagnosis ◽  
Missense Variant ◽  
Functional Domain ◽  
Sequencing Data ◽  
Missense Variants ◽  
Combining Data ◽  
Sequencing Studies ◽  
Scale Population

Objective:To provide new insights into the interpretation of genetic variants in a rare neurologic disorder, CDKL5 deficiency, in the contexts of population sequencing data and an updated characterization of the CDKL5 gene.Methods:We analyzed all known potentially pathogenic CDKL5 variants by combining data from large-scale population sequencing studies with CDKL5 variants from new and all available clinical cohorts and combined this with computational methods to predict pathogenicity.Results:The study has identified several variants that can be reclassified as benign or likely benign. With the addition of novel CDKL5 variants, we confirm that pathogenic missense variants cluster in the catalytic domain of CDKL5 and reclassify a purported missense variant as having a splicing consequence. We provide further evidence that missense variants in the final 3 exons are likely to be benign and not important to disease pathology. We also describe benign splicing and nonsense variants within these exons, suggesting that isoform hCDKL5_5 is likely to have little or no neurologic significance. We also use the available data to make a preliminary estimate of minimum incidence of CDKL5 deficiency.Conclusions:These findings have implications for genetic diagnosis, providing evidence for the reclassification of specific variants previously thought to result in CDKL5 deficiency. Together, these analyses support the view that the predominant brain isoform in humans (hCDKL5_1) is crucial for normal neurodevelopment and that the catalytic domain is the primary functional domain.

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