scholarly journals hts-nim: scripting high-performance genomic analyses

2018 ◽  
Author(s):  
Brent S. Pedersen ◽  
Aaron R. Quinlan
Keyword(s):  
High Performance ◽  
Genomic Data ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Scripting Languages ◽  
Link Type ◽  
Custom Software ◽  
Genomic Analyses ◽  
Biological Insight ◽  
Supplementary Material

AbstractMotivationExtracting biological insight from genomic data inevitably requires custom software. In many cases, this is accomplished with scripting languages, owing to their accessibility and brevity. Unfortunately, the ease of scripting languages typically comes at a substantial performance cost that is especially acute with the scale of modern genomics datasets.ResultsWe present hts-nim, a high-performance library written in the Nim programming language that provides a simple, scripting-like syntax without sacrificing performance.Availabilityhts-nim is available at https://github.com/brentp/hts-nim and the example tools are at https://github.com/brentp/hts-nim-tools both under the MIT [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals hts-nim: scripting high-performance genomic analyses

2018 ◽  
Vol 34 (19) ◽  
pp. 3387-3389 ◽  
Author(s):  
Brent S Pedersen ◽  
Aaron R Quinlan
Keyword(s):  
Programming Language ◽  
High Performance ◽  
Genomic Data ◽  
Scripting Languages ◽  
Custom Software ◽  
Genomic Analyses ◽  
Biological Insight

Abstract Motivation Extracting biological insight from genomic data inevitably requires custom software. In many cases, this is accomplished with scripting languages, owing to their accessibility and brevity. Unfortunately, the ease of scripting languages typically comes at a substantial performance cost that is especially acute with the scale of modern genomics datasets. Results We present hts-nim, a high-performance library written in the Nim programming language that provides a simple, scripting-like syntax without sacrificing performance. Availability and implementation hts-nim is available at https://github.com/brentp/hts-nim and the example tools are at https://github.com/brentp/hts-nim-tools both under the MIT license.

scholarly journals Crosslink: A fast, scriptable genetic mapper for outcrossing species

2017 ◽  
Author(s):  
Robert J. Vickerstaff ◽  
Richard J. Harrison
Keyword(s):  
Large Datasets ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Link Type ◽  
Mapping Software ◽  
Outcrossing Species ◽  
Supplementary Material ◽  
Novel Approaches ◽  
Similar Accuracy ◽  
General Public License

AbstractSummaryCrosslink is genetic mapping software for outcrossing species designed to run efficiently on large datasets by combining the best from existing tools with novel approaches. Tests show it runs much faster than several comparable programs whilst retaining a similar accuracy.Availability and implementationAvailable under the GNU General Public License version 2 from https://github.com/eastmallingresearch/[email protected] informationSupplementary data are available at Bioinformatics online and from https://github.com/eastmallingresearch/crosslink/releases/tag/v0.5.

scholarly journals FQSqueezer: k-mer-based compression of sequencing data

2019 ◽  
Author(s):  
Sebastian Deorowicz
Keyword(s):  
Data Compression ◽  
State Of The Art ◽  
Genomic Data ◽  
General Purpose ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Sequencing Data ◽  
Partial Matching ◽  
Supplementary Material ◽  
Better Than

AbstractMotivationThe amount of genomic data that needs to be stored is huge. Therefore it is not surprising that a lot of work has been done in the field of specialized data compression of FASTQ files. The existing algorithms are, however, still imperfect and the best tools produce quite large archives.ResultsWe present FQSqueezer, a novel compression algorithm for sequencing data able to process single- and paired-end reads of variable lengths. It is based on the ideas from the famous prediction by partial matching and dynamic Markov coder algorithms known from the general-purpose-compressors world. The compression ratios are often tens of percent better than offered by the state-of-the-art tools.Availability and Implementationhttps://github.com/refresh-bio/[email protected] informationSupplementary data are available at publisher’s Web site.

scholarly journals PyRanges: efficient comparison of genomic intervals in Python

2019 ◽  
Author(s):  
Endre Bakken Stovner ◽  
Pål Sætrom
Keyword(s):  
Supplementary Information ◽  
Supplementary Data ◽  
Genomic Libraries ◽  
Link Type ◽  
Simple Set ◽  
Set Operations ◽  
Wide Range ◽  
Genomic Analyses ◽  
Associated Data ◽  
Memory Efficient

AbstractSummaryComplex genomic analyses often use sequences of simple set operations like intersection, overlap, and nearest on genomic intervals. These operations, coupled with some custom programming, allow a wide range of analyses to be performed. To this end, we have written PyRanges, a data structure for representing and manipulating genomic intervals and their associated data in Python. Run single-threaded on binary set operations, PyRanges is in median 2.3-9.6 times faster than the popular R GenomicRanges library and is equally memory efficient; run multi-threaded on 8 cores, our library is up to 123 times faster. PyRanges is therefore ideally suited both for individual analyses and as a foundation for future genomic libraries in Python.AvailabilityPyRanges is available open-source under the MIT license at https://github.com/biocore-NTNU/pyranges and documentation exists at https://biocore-NTNU.github.io/pyranges/[email protected] informationSupplementary data are available.

scholarly journals GTShark: Genotype compression in large project

2018 ◽  
Author(s):  
Sebastian Deorowicz ◽  
Agnieszka Danek
Keyword(s):  
Web Site ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Link Type ◽  
Large Project ◽  
Supplementary Material

AbstractSummaryNowadays large sequencing projects handle tens of thousands of individuals. The huge files summarizing the findings definitely require compression. We propose a tool able to compress large collections of genotypes as well as single samples in such projects to sizes not achievable to date.Availability and Implementationhttps://github.com/refresh-bio/[email protected] informationSupplementary data are available at publisher’s Web site.

scholarly journals Haplotype-aware graph indexes

2019 ◽  
Author(s):  
Jouni Sirén ◽  
Erik Garrison ◽  
Adam M. Novak ◽  
Benedict Paten ◽  
Richard Durbin
Keyword(s):  
Genetic Variation ◽  
Chromosome 17 ◽  
Supplementary Information ◽  
Whole Genome ◽  
Supplementary Data ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Link Type ◽  
Supplementary Material ◽  
Haplotype Information

AbstractMotivationThe variation graph toolkit (VG) represents genetic variation as a graph. Although each path in the graph is a potential haplotype, most paths are nonbiological, unlikely recombinations of true haplotypes.ResultsWe augment the VG model with haplotype information to identify which paths are more likely to exist in nature. For this purpose, we develop a scalable implementation of the graph extension of the positional Burrows–Wheelertransform (GBWT). We demonstrate the scalability of the new implementation by building a whole-genome index of the 5,008 haplotypes of the 1000 Genomes Project, and an index of all 108,070 TOPMed Freeze 5 chromosome 17 haplotypes. We also develop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in the haplotypes.AvailabilityOur software is available at https://github.com/vgteam/vg, https://github.com/jltsiren/gbwt, and https://github.com/jltsiren/[email protected] informationSupplementary data are available.

scholarly journals dms2dfe: Comprehensive Workflow for Analysis of Deep Mutational Scanning Data

2016 ◽  
Author(s):  
Rohan Dandage ◽  
Kausik Chakraborty
Keyword(s):  
Noise Reduction ◽  
High Throughput ◽  
Critical Issue ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Selection Pressures ◽  
Link Type ◽  
Supplementary Material ◽  
End To End ◽  
Python Package

SummaryHigh throughput genotype to phenotype (G2P) data is increasingly being generated by widely applicable Deep Mutational Scanning (DMS) method. dms2dfe is a comprehensive end-to-end workflow that addresses critical issue with noise reduction and offers variety of crucial downstream analyses. Noise reduction is carried out by normalizing counts of mutants by depth of sequencing and subsequent dispersion shrinkage at the level of calculation of preferential enrichments. In downstream analyses, dms2dfe workflow provides identification of relative selection pressures, potential molecular constraints and generation of data-rich visualizations.Availabilitydms2dfe is implemented as a python package and it is available at https://kc-lab.github.io/[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals mirtronDB: a mirtron knowledge base

2018 ◽  
Author(s):  
Bruno Henrique Ribeiro Da Fonseca ◽  
Douglas Silva Domingues ◽  
Alexandre Rossi Paschoal
Keyword(s):  
Knowledge Base ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Knowledge Database ◽  
Group Type ◽  
Link Type ◽  
Supplementary Material ◽  
Access To Knowledge ◽  
User Friendly ◽  
Organism Group

AbstractMotivationMirtrons are originated from short introns with atypical cleavage from the miRNA canonical pathway by using the splicing mechanism. Several studies describe mirtrons in chordates, invertebrates and plants but in the current literature there is no repository that centralizes and organizes these public and available data. To fill this gap, we created the first knowledge database dedicated to mirtron, called mirtronDB, available at http://mirtrondb.cp.utfpr.edu.br/. MirtronDB has a total of 1,407 mirtron precursors and 2,426 mirtron mature sequences in 18 species.ResultsThrough a user-friendly interface, users can browse and search mirtrons by organism, organism group, type and name. MirtronDB is a specialized resource to explore mirtrons and their regulations, providing free, user-friendly access to knowledge on mirtron data.AvailabilityMirtronDB is available at http://mirtrondb.cp.utfpr.edu.br/[email protected] informationSupplementary data are available.

scholarly journals deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

2016 ◽  
Author(s):  
Dengfeng Guan ◽  
Bo Liu ◽  
Yadong Wang
Keyword(s):  
Source Code ◽  
Classification Method ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Supplementary Data ◽  
Link Type ◽  
Memory Footprint ◽  
Supplementary Material ◽  
De Bruijn

AbstractSummaryIn metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy.Availabilitythe C++ source code of deSPI is available at https://github.com/hitbc/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals HAMAP rules as SPARQL A portable annotation pipeline for genomes and proteomes

2019 ◽  
Author(s):  
Jerven Bolleman ◽  
Eduoard de Castro ◽  
Delphine Baratin ◽  
Sebastien Gehant ◽  
Beatrice A. Cuche ◽  
...  
Keyword(s):  
Data Quality ◽  
Protein Sequences ◽  
Cost Effective ◽  
Supplementary Information ◽  
Biological Knowledge ◽  
Supplementary Data ◽  
Annotation Pipeline ◽  
Link Type ◽  
Supplementary Material

AbstractMotivationGenome and proteome annotation pipelines are generally custom built and therefore not easily reusable by other groups, which leads to duplication of effort, increased costs, and suboptimal results. One cost-effective way to increase the data quality in public databases is to encourage the adoption of annotation standards and technological solutions that enable the sharing of biological knowledge and tools for genome and proteome annotation.ResultsWe have translated the rules of our HAMAP proteome annotation pipeline to queries in the W3C standard SPARQL 1.1 syntax and applied them with two off-the-shelf SPARQL engines to UniProtKB/Swiss-Prot protein sequences described in RDF format. This approach is applicable to any genome or proteome annotation pipeline and greatly simplifies their reuse.AvailabilityHAMAP SPARQL rules and documentation are freely available for download from the HAMAP FTP site ftp://ftp.expasy.org/databases/hamap/hamapsparql.tar.gz under a CC-BY-ND 4.0 license. The annotations generated by the rules are under the CC-BY 4.0 [email protected] informationSupplementary data are included at the end of this document.

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