BOL: Related items

shovill: Assemble bacterial isolate genomes from Illumina paired-end reads

BioStar — Sat, 02 Jan 2021 07:05:36 -0600

Shovill is a pipeline which uses SPAdes at its core, but alters the steps before and after the primary assembly step to get similar results in less time. Shovill also supports other assemblers like SKESA, Velvet and Megahit, so you can take advantage of the pre- and post-processing the Shovill provides with those too.

Address of the bookmark: https://github.com/tseemann/shovill

ALPACA: A hybrid strategy for assembly of genomic DNA shotgun sequencing reads.

Seema Singh — Mon, 30 Apr 2018 04:38:40 -0500

ALPACA requires Celera Assembler 8.3 or later. It is recommended to build Celera Assembler from source. (Why? The pre-built binaries CA_8.3rc1 and CA8.3rc2 will work for any large data set.

Detail paper at https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-3927-8

Address of the bookmark: https://github.com/VicugnaPacos/ALPACA

FastGT: an alignment-free method for calling common SNVs directly from raw sequencing reads

Jit — Tue, 28 Jan 2020 03:27:33 -0600

FastGT is a program package for whole-genome genotyping of genome variants directly from raw sequencing reads. It is written in C and runs in Linux. FastGT uses a list of variant-specific k-mer pairs that are unique in human genome, counts the frequency of k-mers in sequencing data and predicts the genotype. All this takes less than 1 hour on average low-cost Linux server.

http://bioinfo.ut.ee/FastGT/

https://github.com/bioinfo-ut/GenomeTester4/

Address of the bookmark: http://bioinfo.ut.ee/FastGT/

CovCal: Coverage / Read Count Calculator

Jit — Wed, 15 Jun 2016 18:08:13 -0500

Coverage / Read Count Calculator

Calculate how much sequencing you need to hit a target depth of coverage (or vice versa).

Instructions: set the read length/configuration and genome size, then select what you want to calculate.

Written by Stephen Turner, based on the Lander-Waterman formula, inspired by a similar calculator written by James Hadfield. Coverage is calculated as C=LN/G and reads as N=CG/L where C = Coverage (X),L = Read length (bp), G = Haploid genome size (bp), and N = Number of reads. Source code on GitHub.

Address of the bookmark: http://apps.bioconnector.virginia.edu/covcalc/

Jabba: Hybrid Error Correction for Long Sequencing Reads

Jit — Fri, 05 Jan 2018 03:58:14 -0600

Jabba is a hybrid error correction tool to correct third generation (PacBio / ONT) sequencing data, using second generation (Illumina) data.

Input

Jabba takes as input a concatenated de Bruijn graph and a set of sequences:

the de Bruijn graph should appear in fasta format with 1 entry per node, the meta information should be in the format:
>NODE
the set of sequences should be in fasta or fastq format. These sequences will be corrected (e.g. PacBio reads). The corrections will be written to a file Jabba fasta.
The output is a file in fasta format with corrections of the long reads, and additionally a file in the input format containing uncorrected reads.

https://github.com/biointec/jabba/wiki

https://almob.biomedcentral.com/articles/10.1186/s13015-016-0075-7

Address of the bookmark: https://github.com/biointec/jabba

Pathway Analysis

Rahul Agarwal — Fri, 03 Oct 2014 08:51:13 -0500

Pathway Analysis is usually performed with aim to enrich the genes with their functional information and reveal the underlying biological mechanisms pursue by genes. Pathway Analysis is not only limited to what biological pathways a particular set of expressed genes follow but also to disclose the relationships between these genes. With availability of more genomics, transcriptomics and proteomics data, interactions between genes involve in multiple pathways become more clear and also relationships between the genes, their transcripts, and their gene products. However, existing tools and dbs mainly based on knowledge driven approach in which pathways will be identified by finding the correlation between the information in one of the pathway knowledge databases (KEGG,Reactome,Panther,BioCarta, Panther,GO,NCI,WikiPathways,etc) and gene expression result for a specific conditions for instance tumor, obesity , cold resistant crops/plants, etc.

Introductory Articles/ppt/sources:

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375

http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf

http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html

http://davetang.org/muse/tag/pathway/

https://www.biostars.org/p/42219/

http://bioinformatics.ca//files/public/Pathways_2014_Module4_v2.pdf

http://bioinformatics.ca//files/public/Pathways_2014_Module2.pdf

Impotant Database and Tools:

GeneMANIA, Cytoscape, IPA and Metacore (Commerical ), Pathway Commons, Reactome ,Panther, BioCyc, WikiPathways, Pathvisio, KEGG, NCI, Stringdb, Amigo, WebGestalt ,ConsensusPathDB ,GSEA,Blast2go

Popular R based tools:

Reactome.db, ReactomePA, ClusterProfiler, Gage, SPIA, topGO, Pathview,DOSE,GOStat

More:

http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+

iSeqQC: a tool for expression-based quality control in RNA sequencing

BioStar — Sun, 16 Feb 2020 08:47:17 -0600

iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers.

http://cancerwebpa.jefferson.edu/iSeqQC/

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3399-8

Address of the bookmark: https://github.com/gkumar09/iSeqQC

GraphMap - A highly sensitive and accurate mapper for long, error-prone reads

Jit — Wed, 07 Jun 2017 04:18:16 -0500

GraphMap - A highly sensitive and accurate mapper for long, error-prone reads http://www.nature.com/ncomms/2016/160415/ncomms11307/full/ncomms11307.html

Features

    Mapping position agnostic to alignment parameters.
    Consistently very high sensitivity and precision across different error profiles, rates and sequencing technologies even with default parameters.
    Circular genome handling to resolve coverage drops near ends of the genome.
    E-value.
    Meaningful mapping quality.
    Various alignment strategies (semiglobal bit-vector and Gotoh, anchored).
    Overlapping of reads for de novo assembly.
    Transcriptome mapping through internal construction of a transcriptome from a given genomic reference and a GTF file.
    ...and much more.

GraphMap is also used as an overlapper in a new de novo genome assembly project called Ra (https://github.com/mariokostelac/ra-integrate).
Ra attempts to create de novo assemblies from raw nanopore and PacBio reads without requiring error correction, for which a highly sensitive overlapper is required.

Currently, development of a new spliced-alignment mode for mapping RNA-seq reads is under way.
Description of the current effort as well as how to reach the experimental implementation can be found here: doc/rnaseq.md.

Address of the bookmark: https://github.com/isovic/graphmap

WhatsHap: fast and accurate read-based phasing

Jit — Mon, 28 May 2018 09:52:16 -0500

WhatsHap is a software for phasing genomic variants using DNA sequencing reads, also called read-based phasing or haplotype assembly. It is especially suitable for long reads, but works also well with short reads.

Features

Very accurate results (Martin et al., WhatsHap: fast and accurate read-based phasing)

Works well with Illumina, PacBio, Oxford Nanopore and other types of reads

It phases SNVs, indels and even “complex” variants (such as TCG → AGAA)

Pedigree phasing mode uses reads from related individuals (such as trios) to improve results and to reduce coverage requirements (Garg et al., Read-Based Phasing of Related Individuals).

WhatsHap is easy to install

It is easy to use: Pass in a VCF and one or more BAM files, get out a phased VCF. Supports multi-sample VCFs.

It produces standard-compliant VCF output by default

If desired, get output that is compatible with ReadBackedPhasing

Open Source (MIT license)

Address of the bookmark: https://whatshap.readthedocs.io/en/latest/

CLARK: Fast, accurate and versatile sequence classification system

Jit — Sat, 15 Feb 2020 01:49:01 -0600

CLARK, a method based on a supervised sequence classification using discriminative k-mers. Considering two distinct specific classification problems (see the article for details), namely (1) the taxonomic classification of metagenomic reads to known bacterial genomes, and (2) the assignment of BAC clones and transcript to chromosome arms/centromeres (in the absence of a finished assembly for the reference genome), CLARK outperforms in classification speed and precision the best state-of-the-art methods.

http://clark.cs.ucr.edu/Spaced/

Address of the bookmark: http://clark.cs.ucr.edu/Spaced/