BOL: Related items

jackalope: A swift, versatile phylogenomic and high-throughput sequencing simulator

Abhimanyu Singh — Fri, 26 Jul 2019 00:58:12 -0500

jackalope simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations—the latter of which can include selection, recombination, and demographic fluctuations. jackalope can simulate single, paired-end, or mate-pair Illumina reads, as well as reads from Pacific Biosciences These simulations include sequencing errors, mapping qualities, multiplexing, and optical/PCR duplicates. All outputs can be written to standard file formats.

A swift, versatile phylogenomic and high-throughput sequencing simulator https://jackalope.lucasnell.com

Address of the bookmark: https://github.com/lucasnell/jackalope

DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

Jit — Sat, 25 Jan 2020 13:28:09 -0600

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant

McClintock: Meta-pipeline to identify transposable element insertions using next generation sequencing data

BioStar — Tue, 27 Oct 2020 00:21:18 -0500

an integrated bioinformatics pipeline for the detection of TE insertions in whole-genome shotgun data, called McClintock (https://github.com/bergmanlab/mcclintock), which automatically runs and standardizes output for multiple TE detection methods. We demonstrate the utility of McClintock by evaluating six TE detection methods using simulated and real genome data from the model microbial eukaryote, Saccharomyces cerevisiae.

Address of the bookmark: https://github.com/bergmanlab/mcclintock

Pollux: platform independent error correction of single and mixed genomes

Jit — Fri, 19 May 2017 09:41:27 -0500

Pollux: General-purpose error corrector that corrects errors introduced by Illumina, Ion Torrent, and Roche 454 sequencing technologies and can be applied to single- or mixed-genome data. In addition to correcting substitution errors, we locate and correct insertion, deletion, and homopolymer errors while remaining sensitive to low coverage areas of sequencing projects. Using published data sets, we correct 94% of Illumina MiSeq errors, 88% of Ion Torrent PGM errors, 85% of Roche 454 GS Junior errors. Introduced errors are 20 to 70 times more rare than successfully corrected errors. Furthermore, we show that the quality of assemblies improves when reads are corrected by our software.

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-014-0435-6

Address of the bookmark: https://github.com/emarinier/pollux

Troyanskaya Lab

Tue, 04 Feb 2020 06:40:36 -0600

The goal of our research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods for systems-level pathway modeling through integrative analysis of genome-scale datasets. We apply these approaches in studying challenging biological problems, such as how pathways function in diverse cell types and how they change dynamically.

https://function.princeton.edu/

Liftoff: an accurate tool that maps annotations in GFF or GTF between assemblies

Jit — Tue, 30 Jun 2020 21:40:52 -0500

Liftoff, an accurate tool that maps annotations in GFF or GTF between assemblies of the same, or closely-related species. Unlike current coordinate lift-over tools which require a pre-generated “chain” file as input, Liftoff is a standalone tool that takes two genome assemblies and a reference annotation as input and outputs an annotation of the target genome.

Address of the bookmark: https://github.com/agshumate/Liftoff

KisSplice

Jit — Tue, 16 Aug 2016 08:34:19 -0500

KisSplice is a software that enables to analyse RNA-seq data with or without a reference genome. It is an exact local transcriptome assembler that allows to identify SNPs, indels and alternative splicing events. It can deal with an arbitrary number of biological conditions, and will quantify each variant in each condition. It has been tested on Illumina datasets of up to 1G reads. Its memory consumption is around 5Gb for 100M reads.

KisSplice is not a full-length transcriptome assembler. This means that it will output the variable regions of the transcripts, not reconstruct them entirely.

KisSplice comes as a workflow, with several possible post-treatments meant to facilitate the analysis of the results. The choice of the post-treatment depends on the availability of a reference genome/transcriptome and on the need to perform a differential analysis, as summarised in the following table.

Address of the bookmark: http://kissplice.prabi.fr/

YASRA: Reference based assembler

Abhimanyu Singh — Wed, 01 Mar 2017 08:32:45 -0600

YASRA (Yet Another Short Read Assembler) performs comparative assembly of short reads using a reference genome, which can differ substantially from the genome being sequenced. Mapping reads to reference genomes makes use of LASTZ (Harris et al), a pairwise sequence aligner compatible with BLASTZ. Special scoring sets were derived to improve the performance, both in runtime and quality for 454 and Illumina sequence reads.

YASRA uses LASTZ (http://bx.psu.edu/miller_lab for released version and http://www.bx.psu.edu/~rsharris/lastz/newer for newer version) for aligning the sequences to the reference genome. Please install LASTZ (the newest version on http://www.bx.psu.edu/~rsharris/lastz/newer) and add the LASTZ binary in your executable/binary search path before installing YASRA.

Address of the bookmark: https://github.com/aakrosh/YASRA

RePS: Repeat-masked Phrap with scaffolding, a WGS sequence assembler

Jit — Sat, 04 Jan 2020 01:08:09 -0600

RePS (Repeat-masked Phrap with scaffolding), a WGS sequence assembler, that explicitly identifies exact kmer repeats from the shotgun data and removes them prior to the assembly. The established software Phrap is used to compute meaningful error probabilities for each base. Clone-end-pairing information is used to construct scaffolds that order and orient the contigs. The updated version of RePS incorporates some of the ideas introduced by Phusion on clustering

More at

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC186573/

Address of the bookmark: ftp://ftp.genomics.org.cn/pub/ricedb/Tools/RePS/RePS-IBM-AIX.tar.gz

Bandage: interactive visualization of de novo genome assemblies

Shruti Paniwala — Mon, 04 Dec 2017 10:09:37 -0600

Bandage (a Bioinformatics Application for Navigating De novo Assembly Graphs Easily) is a tool for visualizing assembly graphs with connections. Users can zoom in to specific areas of the graph and interact with it by moving nodes, adding labels, changing colors and extracting sequences. BLAST searches can be performed within the Bandage graphical user interface and the hits are displayed as highlights in the graph. By displaying connections between contigs, Bandage presents new possibilities for analyzing de novo assemblies that are not possible through investigation of contigs alone.

Availability and implementation: Source code and binaries are freely available at https://github.com/rrwick/Bandage. Bandage is implemented in C++ and supported on Linux, OS X and Windows. A full feature list and screenshots are available at http://rrwick.github.io/Bandage.

Address of the bookmark: http://rrwick.github.io/Bandage/