BOL: Related items

Π-cyc: A Reference-free SNP Discovery Application using Parallel Graph Search

Jit — Tue, 28 Jan 2020 03:34:23 -0600

Reference free SNP search for comparative population genomics: multiple samples run simultanously. **experimental phase, compiles and runs with OpenMPI-1.8.8 with Intel Compiler only

Cycles enumeration (aka Bubbles) as part of de novo de bruijn graphs assembly using colours can be unpractical for large error prone genomes which makes the assembly process produce an excessive number of false positive cycles. Our solution is to search the graph in multicores shared memory parallel mode using graph decomposition then use filtering method to generate good quality SNPs.

https://arxiv.org/abs/1809.06700

https://github.com/redayounsi/2KP2P

/2kp2omp/bin/main_2kp2_K63_C2 -i fastq_files.txt -o fungus_bub.fasta -r stat_fungus.txt -c cov_fungus_hash.txt -k 63 -h 20 -b 100 -g 600 -l 100 -f 16 -t 5.0 -x 1 -v 0 -p 1 -y 1 -u 1

Address of the bookmark: https://github.com/redayounsi/2KP2P

Kevler: Reference-free variant discovery in large eukaryotic genomes

Jit — Tue, 28 Jan 2020 03:21:53 -0600

Welcome to kevlar, software for predicting de novo genetic variants without mapping reads to a reference genome! kevlar's k-mer abundance based method calls single nucleotide variants (SNVs), multinucleotide variants (MNVs), insertion/deletion variants (indels), and structural variants (SVs) simultaneously with a single simple model.

More at https://kevlar.readthedocs.io/en/latest/

https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf

Address of the bookmark: https://github.com/kevlar-dev/kevlar

PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes

BioStar — Thu, 13 Aug 2020 10:06:19 -0500

PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring O(n2) resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.

Address of the bookmark: https://github.com/ryanmelnyk/PyParanoid

S-plot2: Rapid Visual and Statistical Analysis of Genomic Sequences

Abhimanyu Singh — Tue, 02 Oct 2018 17:57:27 -0500

S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot

ClipCrop: a tool for detecting structural variations with single-base resolution using soft-clipping information

BioJoker — Sun, 20 Jan 2019 06:34:36 -0600

ClipCrop for detecting SVs with single-base resolution using soft-clipping information. A soft-clipped sequence is an unmatched fragment in a partially mapped read. To assess the performance of ClipCrop with other SV-detecting tools, we generated various patterns of simulation data – SV lengths, read lengths, and the depth of coverage of short reads – with insertions, deletions, tandem duplications, inversions and single nucleotide alterations in a human chromosome.

Address of the bookmark: https://github.com/shinout/clipcrop

Parliament2: Runs a combination of tools to generate structural variant calls on whole-genome sequencing data

Jit — Thu, 28 May 2020 21:57:03 -0500

Parliament2 identifies structural variants in a given sample relative to a reference genome. These structural variants cover large deletion events that are called as Deletions of a region, Insertions of a sequence into a region, Duplications of a region, Inversions of a region, or Translocations between two regions in the genome.

Parliament2 runs a combination of tools to generate structural variant calls on whole-genome sequencing data. It can run the following callers: Breakdancer, Breakseq2, CNVnator, Delly2, Manta, and Lumpy. Because of synergies in how the programs use computational resources, these are all run in parallel. Parliament2 will produce the outputs of each of the tools for subsequent investigation.

Address of the bookmark: https://github.com/dnanexus/parliament2

Awesome bioinformatics pipelines !

Jitendra Prajapati — Wed, 30 Mar 2016 21:50:41 -0500

A curated list of awesome pipeline toolkits ...

https://github.com/pditommaso/awesome-pipeline

Address of the bookmark: https://github.com/pditommaso/awesome-pipeline

KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies

Jit — Fri, 06 Jul 2018 03:36:45 -0500

KAT is a suite of tools that analyse jellyfish hashes or sequence files (fasta or fastq) using kmer counts. The following tools are currently available in KAT:

hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in output for easy plotting.
gcp: K-mer GC Processor. Creates a matrix of the number of K-mers found given a GC count and a K-mer count.
comp: K-mer comparison tool. Creates a matrix of shared K-mers between two (or three) sequence files or hashes.
sect: SEquence Coverage estimator Tool. Estimates the coverage of each sequence in a file using K-mers from another sequence file.
blob: Given, reads and an assembly, calculates both the read and assembly K-mer coverage along with GC% for each sequence in the assembly.SEquence Coverage estimator Tool.
filter: Filtering tools. Contains tools for filtering k-mer hashes and FastQ/A files:
- kmer: Produces a k-mer hash containing only k-mers within specified coverage and GC tolerances.
- seq: Filters a sequence file based on whether or not the sequences contain k-mers within a provided hash.
plot: Plotting tools. Contains several plotting tools to visualise K-mer and compare distributions. The following plot tools are available:
- density: Creates a density plot from a matrix created with the "comp" tool. Typically this is used to compare two K-mer hashes produced by different NGS reads.
- profile: Creates a K-mer coverage plot for a single sequence. Takes in fasta coverage output coverage from the "sect" tool
- spectra-cn: Creates a stacked histogram using a matrix created with the "comp" tool. Typically this is used to compare a jellyfish hash produced from a read set to a jellyfish hash produced from an assembly. The plot shows the amount of distinct K-mers absent, as well as the copy number variation present within the assembly.
- spectra-hist: Creates a K-mer spectra plot for a set of K-mer histograms produced either by jellyfish-histo or kat-histo.
- spectra-mx: Creates a K-mer spectra plot for a set of K-mer histograms that are derived from selected rows or columns in a matrix produced by the "comp".

In addition, KAT contains a python script for analysing the mathematical distributions present in the K-mer spectra in order to determine how much content is present in each peak.

This README only contains some brief details of how to install and use KAT. For more extensive documentation please visit: https://kat.readthedocs.org/en/latest/

https://academic.oup.com/bioinformatics/article/33/4/574/2664339

Address of the bookmark: https://github.com/TGAC/KAT

Environment for Tree Exploration (ETE) is a Python programming toolkit that assists in the recontruction, manipulation, analysis and visualization of phylogenetic trees

Rahul Nayak — Wed, 27 Nov 2019 05:32:33 -0600

The Environment for Tree Exploration (ETE) is a Python programming toolkit that assists in the recontruction, manipulation, analysis and visualization of phylogenetic trees (although clustering trees or any other tree-like data structure are also supported).

Other tools

https://github.com/shenwei356/taxonkit

ETE, version: 3.1.1
BioPython, version: 1.73
taxadb, version: 0.10.1
TaxonKit, version: 0.5.0

Address of the bookmark: https://pypi.org/project/ete3/3.1.1/

ProteoClade: A taxonomic toolkit for multi-species and metaproteomic analysis

Jit — Wed, 18 Mar 2020 14:27:20 -0500

ProteoClade is a Python library for taxonomic-based annotation and quantification of bottom-up proteomics data. It is designed to be user-friendly, and has been optimized for speed and storage requirements.

ProteoClade helps you analyze two general categories of experiments:

Targeted Database Searches: Experiments in which a limited number of species are defined ahead of time, such as those involving Patient-Derived Xenografts (PDXs) or host-pathogen interactions. Reference protein sequence databases are used for targeted searches (ex: using Mascot, MaxQuant).
De Novo Searches: Experiments in which the organisms are unspecified ahead of time or involve samples of high taxonomic complexity. Mass spectra are analyzed in the absence of a reference database (ex: using PEAKS, PepNovo).

ProteoClade scales from two organisms to every organism in UniProt. Please refer to the complete documentation at proteoclade.readthedocs.io for installation, a user's guide, and examples.

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007741

Address of the bookmark: https://github.com/HeldLab/ProteoClade