BOL: Related items

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

1mb long DNA with Nanopore technology

Jit — Tue, 19 Dec 2017 18:49:28 -0600

The first continuous DNA read of more than a million bases (>1Mb) has been achieved, using Oxford Nanopore sequencing technology. Congratulations to Martin Smith and collaborators! Read more: http://bit.ly/2j5TNCO

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

NanoPack: visualizing and processing long-read sequencing data

Jit — Tue, 25 Dec 2018 21:20:50 -0600

The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.

Address of the bookmark: https://github.com/wdecoster/nanopack

ngs-bits - Short-read sequencing tools

Neel — Thu, 16 Jan 2020 23:14:00 -0600

Binaries of ngs-bits are available via Bioconda. Alternatively, ngs-bits can be built from sources:

Binaries for Linux/macOS
From sources for Linux/macOS
From sources for Windows

Address of the bookmark: https://github.com/imgag/ngs-bits

Understanding your reads and mapping !

Neel — Wed, 29 Jan 2020 06:29:55 -0600

One of the best tutorial for beginners ...

https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

Address of the bookmark: https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

MOSS: A System for Detecting Software Similarity

Jit — Sat, 09 Dec 2017 08:59:07 -0600

Moss (for a Measure Of Software Similarity) is an automatic system for determining the similarity of programs. To date, the main application of Moss has been in detecting plagiarism in programming classes. Since its development in 1994, Moss has been very effective in this role. The algorithm behind moss is a significant improvement over other cheating detection algorithms (at least, over those known to us).

Moss can currently analyze code written in the following languages:

C, C++, Java, C#, Python, Visual Basic, Javascript, FORTRAN, ML, Haskell, Lisp, Scheme, Pascal, Modula2, Ada, Perl, TCL, Matlab, VHDL, Verilog, Spice, MIPS assembly, a8086 assembly, a8086 assembly, MIPS assembly, HCL2.

Address of the bookmark: https://theory.stanford.edu/~aiken/moss/

JRF/SRF at University of Hyderabad

Fri, 17 Oct 2014 01:55:44 -0500

Applications are invited for the following post of Junior Research Fellow (temporary position coterminous with the project) under DBT funded research project on ““Understanding the functions of α1β1γ1/α2β1γ1 selective AMPK Modulators in dissecting the pharmacological role of these isozymes in metabolic diseases”

Qualified and interested candidates can send their curriculum vitae by e-mail to hr@drils.org on or before 27th October 2014 mention in the subject line of the mail the following code: AMPK-Biology.

Selected candidates will be called for a personal interview to Dr. Reddy’s Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad. The selected candidate is expected to report within two weeks from the date of selection to start work on the project.

Junior Research Fellowship (Molecular Modeling/Biology) for two years and Senior Research fellowship for one year

Junior Research Fellowship: Rs. 15,600/- (consolidated) per month for first two years.
Senior Research Fellowship: Rs. 18,200/-(consolidated) per month for the 3rd year.

Duration: The duration of the fellowship is for three years. However, the performance of the candidate will be reviewed after the completion of every year and the fellowship will be renewed only upon satisfactory performance.

Responsibilities:

1) Literature search.
2) Design, plan and execute experiments under the supervision of the scientist.
3) Provide scientific support to the scientist in his/her research activities.
4) Book keeping and maintenance of stocks and consumables.

Essential Qualifications:

Required: M.Sc. in Microbiology/Biotechnology/Bioinformatics or any other related branch of basic Sciences from a recognized university/institute with a consistent academic record of minimum 60% aggregate in all qualifying examinations. The candidate should be NET qualified for lectureship. The candidate should be motivated to work with dedication.

Desirable: expertise/experience in both Molecular Modeling and Molecular Biology.

Experience: 0-2 years in the areas of Molecular Modeling and/or Molecular Biology and cell biology and Biochemistry.

Preferable: Relevant research experience as evident from thesis/dissertation/project work.

Advertisement: http://www.ilsresearch.org/userfiles/Junior%20REsearch%20Fellowship%20-%20AMPK(Biology).pdf

LRCstats: Long Read Correction Statistics

Jit — Fri, 05 Jan 2018 04:04:20 -0600

LRCstats is an open-source pipeline for benchmarking DNA long read correction algorithms for long reads outputted by third generation sequencing technology such as machines produced by Pacific Biosciences. The reads produced by third generation sequencing technology, as the name suggests, are longer in length than reads produced by next generation sequencing technologies, such as those produced by Illumina. However, long reads are plagued by high error rates, which can cause issues in downstream analysis. Long read correction algorithms reduce the error rate of long reads either through self-correcting methods or using accurate, short reads outputted by next generation sequencing technologies to correct long reads.

Of course, some long read correction algorithms are better than others, and developers of long read correction algorithms will wish to compare their algorithm with others currently available. LRCstats benchmarks long read correction algorithms using long reads produced by simulators (such as SimLoRD or PBSim) where the two-way alignments between the uncorrected long reads (uLR) and the corresponding sequences in the reference genome (Ref) are given in some sort of alignment file and then aligning the corrected long reads (cLR) to the Ref-uLR two-way alignments to create three-way alignments using a dynamic programming algorithm. Statistics on these three-way alignments are then collected, such as the overall error rates of the corrected long reads.

https://www.healthcare.uiowa.edu/labs/au/LSC/

Address of the bookmark: https://github.com/cchauve/lrcstats