BOL: Related items

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/

FiNGS: Filters for Next Generation Sequencing

Neel — Sat, 27 Feb 2021 01:18:35 -0600

Key features

Filters SNVs from any variant caller to remove false positives
Calculates metrics based on BAM files and provides filtering not possible with other tools
Fully user-configurable filtering (including which filters to use and their thresholds)
Option to use filters identical to ICGC recommendations

FiNGS provides researchers with a tool to reproducibly filter somatic variants that is simple to both deploy and use, with filters and thresholds that are fully configurable by the user. It ingests and emits standard variant call format (VCF) files and will slot into existing sequencing pipelines. It allows users to develop and implement their own filtering strategies and simple sharing of these with others.

FiNGS reliably improves upon the precision of default variant caller outputs and performs better than other tools designed for the same task.

Address of the bookmark: https://github.com/cpwardell/FiNGS

Libraries or management tools for high throughput sequencing data

LEGE — Fri, 04 Oct 2024 02:45:06 -0500

GATB Library. The Genome Analysis Toolbox with de-Bruijn graph. A large part of tools developed by the GenScale team are based on this library.
These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (e.g. metagenomes). Among them are (the full is available here: https://gatb.inria.fr/software/):
LRez: C++ Library and toolkit for the barcode-based management and indexation of linked-read datasets.

Variant calling and/or genotyping

DiscoSNP++ and discoSnpRAD: Reference-free small variant discovery (SNPs and indels)
MindTheGap: Detection and assembly of large insertion variants
TakeABreak: reference-free inversion discovery tool
SVJedi: Structural Variant genotyper with long read data
SVJedi-graph: Structural Variant genotyper with long read data using a variation graph

Sequence assembly

MinYS: reference-guided genome assembly in metagenomics data
MTG-link: local assembly tool for linked-read data
Minia: De novo short read assembler
de-novo pipeline: de-novo assembly pipeline (error correction / contigs / scaffolding) for genomes and meta-genomes
Mapsembler2: Targeted assembly (not maintained)

Managing k-mers & indexation

findere: simple strategy for speeding up queries and for reducing false positive calls from any Approximate Membership Query data structure.
- fimpera extends findere adding the abundance information.
kmtricks: modular tool suite for counting kmers, and constructing Bloom filters or kmer matrices, for large collections of sequencing data.
kmindex is a tool for indexing and querying sequencing samples. It is built on top of kmtricks.
back to sequences: Find sequences (reads, unitigs, genes) related to a set of kmers in large datasets, in a matter of seconds.
Backpack Quotient Filter: k-mer indexing data structure with abundance
short read connector: Detect similar reads from potentially large read set
DSK: Count K-mer in sequences

Pangenome graph manipulation

Pancat: Pangenome Comparison and Analysis Toolkit
GFAGraphs: a Python library to handle pangenome graph files in GFA format.

Comparative metagenomics with k-mers

Simka and SimkaMin: Comparative metagenomics for large-scale datasets
Comparead & Commet: comparison of metagenomic datasets

Species and bacterial strains identification

ORI: software using long nanopore reads to identify bacteria present in a sample at the strain level
StrainFLAIR: STRAIN-level proFiLing using vArIation gRaph

General-purpose sequencing data manipulation

GASSST: long read mapper
Leon: short read compressor (now included in GATB-core)
Bloocoo: short read corrector
BCALM: Construct compacted de Bruijn graphs (unitigs)

Protein Structure

A_Purva: Contact Map Overlap solver
MD-Jeep: Distance Geometry solver
CSA: Comparative Structural Alignment

Workflow

SLICEE: parallel execution of bioinformatics workflows

Comparative Genomics

CASSIS: detection of rearrangement breakpoints
PLAST: intensive bank-to-bank sequence comparison
DRJBreakpointFinder: detection and precise localization of excision sites in proviral segments

List of bioinformatics workflow management tools !

Rahul Nayak — Sat, 20 Mar 2021 00:15:25 -0500

Here are list of Workflow Managers

BigDataScript – A cross-system scripting language for working with big data pipelines in computer systems of different sizes and capabilities. [ paper-2014 | web ]
Bpipe – A small language for defining pipeline stages and linking them together to make pipelines. [ web ]
Common Workflow Language – a specification for describing analysis workflows and tools that are portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. [ web ]
Cromwell – A Workflow Management System geared towards scientific workflows. [ web ]
Galaxy – a popular open-source, web-based platform for data intensive biomedical research. Has several features, from data analysis to workflow management to visualization tools. [ paper-2018 | web ]
Nextflow (recommended) – A fluent DSL modelled around the UNIX pipe concept, that simplifies writing parallel and scalable pipelines in a portable manner. [ paper-2018 | web ]
Ruffus – Computation Pipeline library for python widely used in science and bioinformatics. [ paper-2010 | web ]
SeqWare – Hadoop Oozie-based workflow system focused on genomics data analysis in cloud environments. [ paper-2010 | web ]
Snakemake – A workflow management system in Python that aims to reduce the complexity of creating workflows by providing a fast and comfortable execution environment. [ paper-2018 | web ]
Workflow Descriptor Language – Workflow standard developed by the Broad. [ web ]

Qualimap2: Evaluating next generation sequencing alignment data

Jit — Tue, 11 Sep 2018 04:44:29 -0500

Qualimap 2 is a platform-independent application written in Java and R that provides both a Graphical User Inteface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.

Supported types of experiments include:

Whole-genome sequencing
Whole-exome sequencing
RNA-seq (speical mode available)
ChIP-seq

Address of the bookmark: http://qualimap.bioinfo.cipf.es/

Pacasus: Correction of palindromes in long reads from PacBio and Nanopore

BioStar — Mon, 12 Nov 2018 05:26:48 -0600

Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. Check the poster from the Revolutionizing Next-Generation Sequencing (2nd edition) conference in the source folder: https://github.com/swarris/Pacasus/blob/master/vib2017.pdf.

The prepint version is found on http://www.biorxiv.org/content/early/2017/08/09/173872

It uses the pyPaSWAS framework for sequence alignment (https://github.com/swarris/pyPaSWAS)

Address of the bookmark: https://github.com/swarris/Pacasus

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

mutatrix: a population genome simulator which generates simulated genomes.

Jit — Tue, 28 Jan 2020 04:06:58 -0600

genome simulation across a population with zeta-distributed allele frequency, snps, insertions, deletions, and multi-nucleotide polymorphisms

More at https://github.com/ekg/mutatrix

./mutatrix -S sample -P test/ -p 2 -n 10 reference.fasta

Address of the bookmark: https://github.com/ekg/mutatrix

Perl Special Vars Quick Reference

Abhimanyu Singh — Tue, 07 Feb 2017 05:08:47 -0600

`$_`	The default or implicit variable.
`@_`	Subroutine parameters.
`$a` `$b`	sort comparison routine variables.
`@ARGV`	The command-line args.
Regular Expressions
`$`	Regexp parenthetical capture holders.
`$&`	Last successful match (degrades performance).
`${^MATCH}`	Similar to `$&` without performance penalty. Requires /p modifier.
$`	Prematch for last successful match string (degrades performance).
`${^PREMATCH}`	Similar to $` without performance penalty. Requires `/p` modifier.
`$'`	Postmatch for last successful match string (degrades performance).
`${^POSTMATCH}`	Similar to `$'` without performance penalty. Requires `/p` modifier.
`$+`	Last paren match.
`$^N`	Last closed paren match (last submatch).
`@+`	Offsets of ends of successful submatches in scope.
`@-`	Offsets of starts of successful submatches in scope.
`%+`	Like `@+`, but for named submatches.
`%-`	Like `@-`, but for named submatches.
`$^R`	Last regexp (?{code}) result.
`${^RE_DEBUG_FLAGS}`	Current value of regexp debugging flags. See `use re 'debug';`
`${^RE_TRIE_MAXBUF}`	Control memory allocations for RE optimizations for large alternations.
Encoding
`${^ENCODING}`	The object reference to the Encode object, used to convert the source code to Unicode.
`${^OPEN}`	Internal use: \0 separated Input / Output layer information.
`${^UNICODE}`	Read-only Unicode settings.
`${^UTF8CACHE}`	State of the internal UTF-8 offset caching code.
`${^UTF8LOCALE}`	Indicates whether UTF8 locale was detected at startup.
IO and Separators
`$.`	Current line number (or record number) of most recent filehandle.
`$/`	Input record separator.
`$\|`	Output autoflush. 1=autoflush, 0=default. Applies to currently selected handle.
`$,`	Output field separator (lists)
`$\`	Output record separator.
`$"`	Output list separator. (interpolated lists)
`$;`	Subscript separator. (Use a real multidimensional array instead.)
Formats
`$%`	Page number for currently selected output channel.
`$=`	Current page length.
`$-`	Number of lines left on page.
`$~`	Format name.
`$^`	Name of top-of-page format.
`$:`	Format line break characters
`$^L`	Form feed (default "\f").
`$^A`	Format Accumulator
Status Reporting
`$?`	Child error. Status code of most recent system call or pipe.
`$!`	Operating System Error. (What just went 'bang'?)
`%!`	Error number hash
`$^E`	Extended Operating System Error (Extra error explanation).
`$@`	Eval error.
`${^CHILD_ERROR_NATIVE}`	Native status returned by the last pipe close, backtick (`` ) command, successful call to wait() or waitpid(), or from the system() operator.
ID's and Process Information
`$$`	Process ID
`$<`	Real user id of process.
`$>`	Effective user id of process.
`$(`	Real group id of process.
`$)`	Effective group id of process.
`$0`	Program name.
`$^O`	Operating System name.
Perl Status Info
`$]`	Old: Version and patch number of perl interpreter. Deprecated.
`$^C`	Current value of flag associated with -c switch.
`$^D`	Current value of debugging flags
`$^F`	Maximum system file descriptor.
`$^I`	Value of the -i (inplace edit) switch.
`$^M`	Emergency Memory pool.
`$^P`	Internal variable for debugging support.
`$^R`	Last regexp (?{code}) result.
`$^S`	Exceptions being caught. (eval)
`$^T`	Base time of program start.
`$^V`	Perl version.
`$^W`	Status of -w switch
`${^WARNING_BITS}`	Current set of warning checks enabled by `use warnings;`
`$^X`	Perl executable name.
`${^GLOBAL_PHASE}`	Current phase of the Perl interpreter.
`$^H`	Internal use only: Hook into Lexical Scoping.
`%^H`	Internaluse only: Useful to implement scoped pragmas.
`${^TAINT}`	Taint mode read-only flag.
`${^WIN32_SLOPPY_STAT}`	If true on Windows `stat()` won't try to open the file.
Command Line Args
`ARGV`	Filehandle iterates over files from command line (see also `<>`).
`$ARGV`	Name of current file when reading <>
`@ARGV`	List of command line args.
`ARGVOUT`	Output filehandle for -i switch
Miscellaneous
`@F`	Autosplit (-a mode) recipient.
`@INC`	List of library paths.
`%INC`	Keys are filenames, values are paths to modules included via `use, require,` or `do`.
`%ENV`	Hash containing current environment variables
`%SIG`	Signal handlers.
`$[`	Array and substr first element (Deprecated!).

See perlvar for detailed descriptions of each of these (and a few more) special variables.

NGS course Medical Genomics, scheduled for 17-20 September 2013 in UZ Leuven (Belgium).

Poonam Mahapatra — Mon, 12 Aug 2013 12:08:24 -0500

This course is open to all students and postdocs and registration for all academic participants is free of charge. To help us in organizing the course, please register online via http://gc.uzleuven.be where the preliminary program is also available.

This course is organized with support from the IAP “Belgian Medical Genomics Initiative”, SymBioSys and the Genomics Core.

For inquiries, please email Ms Narcisse Opdekamp ( narcisse.opdekamp@uzleuven.be ).

More at >> http://gc.uzleuven.be/