BOL: Related items

Bioinformatics tools and software

Jit — Tue, 05 Jul 2016 10:02:26 -0500

USEARCH >
Extreme high-throughput sequence analysis. Orders of magnitude faster than BLAST. MUSCLE >
Multiple sequence alignment. Faster and more accurate than CLUSTALW.

UPARSE >
OTU clustering for 16S and other marker genes. Highly accurate OTU sequences and improved diversity measures. UCHIME >
Chimeric sequence detection. PILER >
De novo genome repeat finder. PILER-CR >
Detection of CRISPR repeats in bacterial genomes. QSCORE >
Compare two multiple alignments for benchmarking. PALS >
Whole-genome alignment. PREFAB >
Protein Reference Alignment Database. MSA benchmark collection >
Selected multiple alignment benchmarks in a standardized FASTA format.

Address of the bookmark: http://drive5.com/software.html

Jvarkit : Java utilities for Bioinformatics

Jit — Fri, 08 Jun 2018 09:31:55 -0500

Collection of Java tool kits for bioinformatics works: Jvarkit : Java utilities for Bioinformatics

Address of the bookmark: http://lindenb.github.io/jvarkit/

List of non-commercial NGS genotype-calling software

Jit — Thu, 09 Aug 2018 04:21:32 -0500

Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data.

A list of programs for genotype and SNP calling :

SOAP2 http://soap.genomics.org.cn/index.html

Single-sample High-quality variant database (for example, dbSNP) Package for NGS data analysis, which includes a single individual genotype caller (SOAPsnp)

realSFS http://128.32.118.212/thorfinn/realSFS/

Single-sample Aligned reads Software for SNP and genotype calling using single individuals and allele frequencies. Site frequency spectrum (SFS) estimation

Samtools http://samtools.sourceforge.net/

Multi-sample Aligned reads Package for manipulation of NGS alignments, which includes a computation of genotype likelihoods (samtools) and SNP and genotype calling (bcftools)

GATK http://www.broadinstitute.org/gsa/wiki/index.php/The_Genome_Analysis_Toolkit Multi-sample Aligned reads Package for aligned NGS data analysis, which includes a SNP and genotype caller (Unifed Genotyper), SNP filtering (Variant Filtration) and SNP quality recalibration (Variant Recalibrator)

Beagle http://faculty.washington.edu/browning/beagle/beagle.html

Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation, phasing and association that includes a mode for genotype calling

IMPUTE2 http://mathgen.stats.ox.ac.uk/impute/impute_v2.html

Multi-sample LD Candidate SNPs, genotype likelihoods Software for imputation and phasing, including a mode for genotype calling. Requires fine-scale linkage map

QCall ftp://ftp.sanger.ac.uk/pub/rd/QCALL

Multi-sample LD ‘Feasible’ genealogies at a dense set of loci, genotype likelihoods Software for SNP and genotype calling, including a method for generating candidate SNPs without LD information (NLDA) and a method for incorporating LD information (LDA). The ‘feasible’ genealogies can be generated using Margarita (http://www.sanger.ac.uk/resources/software/margarita)

MaCH http://genome.sph.umich.edu/wiki/Thunder

Multi-sample LD Genotype likelihoods Software for SNP and genotype calling, including a method (GPT_Freq) for generating candidate SNPs without LD information and a method (thunder_glf_freq) for incorporating LD information

Shasta long read assembler

Jit — Tue, 14 Jan 2020 06:47:07 -0600

The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by Oxford Nanopore flow cells.

Computational methods used by the Shasta assembler include:

Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

More at https://chanzuckerberg.github.io/shasta/index.html

Address of the bookmark: https://github.com/chanzuckerberg/shasta

VariantBam: Filtering and profiling of next-generational sequencing data using region-specific rules

Rahul Nayak — Thu, 04 Oct 2018 16:30:44 -0500

VariantBam is a tool to extract/count specific sets of sequencing reads from next-generational sequencing files. To save money, disk space and I/O, one may not want to store an entire BAM on disk. In many cases, it would be more efficient to store only those read-pairs or reads who intersect some region around the variant locations. Alternatively, if your scientific question is focused on only one aspect of the data (e.g. breakpoints), many reads can be removed without losing the information relevant to the problem.

Address of the bookmark: https://github.com/broadinstitute/VariantBam

nQuire: A statistical framework for ploidy estimation using NGS short-read data

Jit — Thu, 31 Jan 2019 05:12:19 -0600

nQuire implements a set of commands to estimate ploidy level of individuals from species, where recent polyploidization occurred and intraspecific ploidy variation is observed. Specifically, nQuire uses next-generation sequencing data to distinguish between diploids, triploids and tetraploids, on the basis of frequency distributions at variant sites where only two bases are segregating.

For more background see also the publication at BMC Bioinformatics.

https://github.com/clwgg/nQuire

Address of the bookmark: https://github.com/clwgg/nQuire

LoFreq*: A sequence-quality aware, ultra-sensitive variant caller for NGS data

BioStar — Tue, 18 Feb 2020 03:24:22 -0600

LoFreq* (i.e. LoFreq version 2) is a fast and sensitive variant-caller for inferring SNVs and indels from next-generation sequencing data. It makes full use of base-call qualities and other sources of errors inherent in sequencing (e.g. mapping or base/indel alignment uncertainty), which are usually ignored by other methods or only used for filtering.

https://github.com/CSB5/lofreq

http://csb5.github.io/lofreq/installation/

https://github.com/CSB5/lofreq/tree/master/dist

Address of the bookmark: http://csb5.github.io/lofreq/

FGENESH - Program for predicting multiple genes in genomic DNA sequences

BioStar — Thu, 20 Dec 2018 11:55:08 -0600

FGENESH is the fastest (50-100 times faster than GenScan) and most accurate gene finder available - see the figure and the table below. In recent rice genome sequencing projects, it was cited "the most successful (gene finding) program (Yu et al. (2002) Science 296:79) and was used to produce 87% of all high-evidence predicted genes (Goff et al. (2002) Science 296:79).

Address of the bookmark: http://www.softberry.com/berry.phtml?topic=fgenesh&group=help&subgroup=gfind

Sequence Tube Maps: displays multiple genomic sequences in the form of a tube map

Jit — Wed, 11 Mar 2020 01:12:06 -0500

A JavaScript module for the visualization of genomic sequence graphs. It automatically generates a "tube map"-like visualization of sequence graphs which have been created with vg. (https://github.com/vgteam/vg)

Link to working demo: https://vgteam.github.io/sequenceTubeMap/

Address of the bookmark: https://github.com/vgteam/sequenceTubeMap

Mulan: MUltiple sequence Local AligNment and conservation visualization tool

Rahul Nayak — Thu, 20 Jul 2017 08:02:32 -0500

Mulan performs multiple (2 or more) sequence alignments with an efficient and rapid "full local" alignment strategy that ensures a recapitulation of evolutionary sequence rearrangements (such as inversions and reshuffling) in any of the species. It combines refine and tba tools to align either "draft" or "finished" quality sequences. Mulan provides a dynamic graphical interface to align and visualize conservation profiles for evolutionarily distant and closely related species.

Input formats, automated data upload from the UCSC Genome Browser, gene annotation, annotation of repetitive elements, and progress report were previously described in the zPicture instructions and we refer the users to these materials for more details. This introduction is mainly focused on some novel features unique to the Mulan.

Address of the bookmark: https://mulan.dcode.org/mulanInstructions.php