scholarly journals Twelve years of SAMtools and BCFtools

GigaScience ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Petr Danecek ◽  
James K Bonfield ◽  
Jennifer Liddle ◽  
John Marshall ◽  
Valeriu Ohan ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Source Code ◽  
Variant Calling ◽  
File Format ◽  
Sequencing Data ◽  
Software Projects ◽  
Effect Analysis ◽  
Commercial Use ◽  
High Throughput Sequencing Data ◽  
File Format Conversion

Abstract Background SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. Findings The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. Conclusion Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.

scholarly journals iSVP: an integrated structural variant calling pipeline from high-throughput sequencing data

2013 ◽  
Vol 7 (Suppl 6) ◽  
pp. S8 ◽  
Author(s):  
Takahiro Mimori ◽  
Naoki Nariai ◽  
Kaname Kojima ◽  
Mamoru Takahashi ◽  
Akira Ono ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Variant Calling ◽  
Sequencing Data ◽  
Structural Variant ◽  
High Throughput Sequencing Data

scholarly journals Bazam: A rapid method for read extraction and realignment of high throughput sequencing data

2018 ◽  
Author(s):  
Simon P Sadedin ◽  
Alicia Oshlack
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Variant Calling ◽  
Genomic Data ◽  
Selective Extraction ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Time Required ◽  
Genomic Regions ◽  
Reference Genomes

AbstractBackgroundAs costs of high throughput sequencing have fallen, we are seeing vast quantities of short read genomic data being generated. Often, the data is exchanged and stored as aligned reads, which provides high compression and convenient access for many analyses. However, aligned data becomes outdated as new reference genomes and alignment methods become available. Moreover, some applications cannot utilise pre-aligned reads at all, necessitating conversion back to raw format (FASTQ) before they can be used. In both cases, the process of extraction and realignment is expensive and time consuming.FindingsWe describe Bazam, a tool that efficiently extracts the original paired FASTQ from reads stored in aligned form (BAM or CRAM format). Bazam extracts reads in a format that directly allows realignment with popular aligners with high concurrency. Through eliminating steps and increasing the accessible concurrency, Bazam facilitates up to a 90% reduction in the time required for realignment compared to standard methods. Bazam can support selective extraction of read pairs from focused genomic regions, further increasing efficiency for targeted analyses. Bazam is additionally suitable as a base for other applications that require efficient paired read information, such as quality control, structural variant calling and alignment comparison.ConclusionsBazam offers significant improvements for users needing to realign genomic data.

scholarly journals Joint variant andde novomutation identification on pedigrees from high-throughput sequencing data

2014 ◽  
Author(s):  
John G Cleary ◽  
Ross Braithwaite ◽  
Kurt Gaastra ◽  
Brian S Hilbush ◽  
Stuart Inglis ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Cost Optimization ◽  
Snp Array ◽  
Variant Calling ◽  
Ground Truth ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Mendelian Segregation ◽  
High Throughput Sequencing Data

The analysis of whole-genome or exome sequencing data from trios and pedigrees has being successfully applied to the identification of disease-causing mutations. However, most methods used to identify and genotype genetic variants from next-generation sequencing data ignore the relationships between samples, resulting in significant Mendelian errors, false positives and negatives. Here we present a Bayesian network framework that jointly analyses data from all members of a pedigree simultaneously using Mendelian segregation priors, yet providing the ability to detectde novomutations in offspring, and is scalable to large pedigrees. We evaluated our method by simulations and analysis of WGS data from a 17 individual, 3-generation CEPH pedigree sequenced to 50X average depth. Compared to singleton calling, our family caller produced more high quality variants and eliminated spurious calls as judged by common quality metrics such as Ti/Tv, Het/Hom ratios, and dbSNP/SNP array data concordance. We developed a ground truth dataset to further evaluate our calls by identifying recombination cross-overs in the pedigree and testing variants for consistency with the inferred phasing, and we show that our method significantly outperforms singleton and population variant calling in pedigrees. We identify all previously validatedde novomutations in NA12878, concurrent with a 7X precision improvement. Our results show that our method is scalable to large genomics and human disease studies and allows cost optimization by rational sequencing capacity distribution.

scholarly journals SomVarIUS: somatic variant identification from unpaired tissue samples

2015 ◽  
Vol 32 (6) ◽  
pp. 808-813 ◽  
Author(s):  
Kyle S. Smith ◽  
Vinod K. Yadav ◽  
Shanshan Pei ◽  
Daniel A. Pollyea ◽  
Craig T. Jordan ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Variant Calling ◽  
Computational Method ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Somatic Variant ◽  
Tissue Samples ◽  
Normal Tissues ◽  
High Throughput Sequencing Data ◽  
Oncogenic Mutations

Abstract Motivation: Somatic variant calling typically requires paired tumor-normal tissue samples. Yet, paired normal tissues are not always available in clinical settings or for archival samples. Results: We present SomVarIUS, a computational method for detecting somatic variants using high throughput sequencing data from unpaired tissue samples. We evaluate the performance of the method using genomic data from synthetic and real tumor samples. SomVarIUS identifies somatic variants in exome-seq data of  ∼150 ×  coverage with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples. We demonstrate the utility of SomVarIUS by identifying somatic mutations in formalin-fixed samples, and tracking clonal dynamics of oncogenic mutations in targeted deep sequencing data from pre- and post-treatment leukemia samples. Availability and implementation: SomVarIUS is written in Python 2.7 and available at http://www.sjdlab.org/resources/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

scholarly journals Comparing Variant Call Files for Performance Benchmarking of Next-Generation Sequencing Variant Calling Pipelines

2015 ◽  
Author(s):  
John G. Cleary ◽  
Ross Braithwaite ◽  
Kurt Gaastra ◽  
Brian S Hilbush ◽  
Stuart Inglis ◽  
...  
Keyword(s):  
Gold Standard ◽  
High Throughput Sequencing ◽  
Variant Calling ◽  
False Positives ◽  
Sequencing Data ◽  
Variant Call ◽  
Performance Benchmarking ◽  
High Throughput Sequencing Data ◽  
Accurate Performance ◽  
Standard Output

To evaluate and compare the performance of variant calling methods and their confidence scores, comparisons between a test call set and a ?gold standard? need to be carried out. Unfortunately, these comparisons are not straightforward with the current Variant Call Files (VCF), which are the standard output of most variant calling algorithms for high-throughput sequencing data. Comparisons of VCFs are often confounded by the different representations of indels, MNPs, and combinations thereof with SNVs in complex regions of the genome, resulting in misleading results. A variant caller is inherently a classification method designed to score putative variants with confidence scores that could permit controlling the rate of false positives (FP) or false negatives (FN) for a given application. Receiver operator curves (ROC) and the area under the ROC (AUC) are efficient metrics to evaluate a test call set versus a gold standard. However, in the case of VCF data this also requires a special accounting to deal with discrepant representations. We developed a novel algorithm for comparing variant call sets that deals with complex call representation discrepancies and through a dynamic programing method that minimizes false positives and negatives globally across the entire call sets for accurate performance evaluation of VCFs.

scholarly journals Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yuhua Fu ◽  
Pengyu Fan ◽  
Lu Wang ◽  
Ziqiang Shu ◽  
Shilin Zhu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Target Genes ◽  
Target Prediction ◽  
Large Data ◽  
Sequencing Data ◽  
Regulate Gene Expression ◽  
High Throughput Sequencing Data ◽  
Annotation Information ◽  
Public Data ◽  
Broad Variety

Abstract Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.

scholarly journals deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data

2020 ◽  
Vol 49 (D1) ◽  
pp. D877-D883
Author(s):  
Fangzhou Xie ◽  
Shurong Liu ◽  
Junhao Wang ◽  
Jiajia Xuan ◽  
Xiaoqin Zhang ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Clinical Information ◽  
Sequencing Data ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
High Throughput Sequencing Data ◽  
Expression Atlas ◽  
Expression Evolution ◽  
Noninvasive Biomarkers ◽  
Cancer Tissues

Abstract Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

scholarly journals Improving gene function predictions using independent transcriptional components

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Carlos G. Urzúa-Traslaviña ◽  
Vincent C. Leeuwenburgh ◽  
Arkajyoti Bhattacharya ◽  
Stefan Loipfinger ◽  
Marcel A. T. M. van Vugt ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
High Throughput Sequencing ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Sequencing Data ◽  
New Members ◽  
High Throughput Sequencing Data ◽  
Gene Sets ◽  
Functional Understanding

AbstractThe interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.

Sign in / Sign up

Export Citation Format

Share Document