BOL: Related items

Free Genomics data !

BioStar — Fri, 07 Feb 2020 14:08:31 -0600

The specimens were collected by the Oxford Wytham Woods and Edinburgh Lohse lab teams. DNA extraction and sequencing was carried out by the Sanger Institute Scientific Operations teams. Assemblies were carried out by the Tree of Life team (Shane McCarthy) and colleagues in Pacific Biosciences (Jonas Korlach).

https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/

Address of the bookmark: https://www.darwintreeoflife.org/an-initial-set-of-raw-genome-assemblies-from-the-darwin-tree-of-life-project/

Senior Bioinformatician (Assembly) Moore Aquatic Symbiosis Project Tree of Life

Sat, 02 Oct 2021 00:28:30 -0500

You will have some previous experience with genome bioinformatics or other large scale scientific data analysis, or a newly qualified graduate student with data science skills interested in DNA sequence data. While desirable, previous experience with DNA sequencing data is not strictly necessary for the position. We have a strong publication record and culture of producing open data resources and open source software development. This role requires an investigative and solution-oriented mindset and excellent communication skills to work effectively within large national and international consortia.

More at https://jobs.sanger.ac.uk/vacancy/senior-bioinformatician-assembly-moore-aquatic-symbiosis-project-tree-of-life-458923.html

PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach

Rahul Nayak — Tue, 05 Jun 2018 09:57:11 -0500

PERGA - Paired End Reads Guided Assembler PERGA is a novel sequence reads guided de novo assembly approach which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds. Instead of using single-end reads to construct contig, PERGA uses paired-end reads and different read overlap sizes from O ≥ Omax to Omin to resolve the gaps and branches. Moreover, by constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. PERGA will try to extend the contigs by all feasible nucleotides and determine if these multiple extensions due to sequencing errors or repeats by using looking ahead technology, and it also try to separate the different repeats of nearby genomic regions to make the assembly result more longer and accurate. The simulated E.coli paired-end reads data are generated using GemSim (KE McElroy, F Luciani, T Thomas. Gemsim: General, Error-Model Based Simulator of Next-Generation Sequencing Data. BMC Genomics 2012, 13:74), with coverage 50x, 60x, 100x, read lengths 100-bp, and can be downloaded from https://github.com/zhuxiao/data_PERGA.

Address of the bookmark: https://github.com/hitbio/PERGA

ContigExtender: a new approach to improving de novo sequence assembly for viral metagenomics data

LEGE — Wed, 08 May 2024 07:32:45 -0500

ContigExtender, was developed to extend contigs, complementing de novo assembly. ContigExtender employs a novel recursive Overlap Layout Candidates (r-OLC) strategy that explores multiple extending paths to achieve longer and highly accurate contigs. ContigExtender is effective for extending contigs significantly in in silico synthesized and real metagenomics datasets.

More at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7953547/

Address of the bookmark: https://github.com/dengzac/contig-extender

Hagfish - assess an assembly through creative use of coverage plots

Abhi — Fri, 20 May 2016 19:08:17 -0500

Hagfish is a tool that is to be used in data analysis of Next Generation Sequencing (NGS) experiments. Hagfish builds on the concept of coverage plots and aims to assist (amongst others) in quality control of de novo genome assembly or identification of structural variation in a genome re-sequencing experiment.

Hagfish requires a reference sequence and a paired end re-sequencing data set. Hagfish has more power the larger the insert size of the paired end library is.

Quick links: Installation,Operation, Read mappers, Hagfish scripts, Hagfish plots

Address of the bookmark: https://github.com/mfiers/hagfish

Alignment-free sequence comparison tools available for next-generation sequencing data analysis

Abhimanyu Singh — Tue, 07 Nov 2017 05:33:33 -0600

kallisto

Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)

Software (C++)

https://pachterlab.github.io/kallisto/

Sailfish

Estimation of isoform abundances from reference sequences and RNA-seq data (k-mer based)

Software (C++)

http://www.cs.cmu.edu/~ckingsf/software/sailfish/

Salmon

Quantification of the expression of transcripts using RNA-seq data (uses k-mers)

https://combine-lab.github.io/salmon/

RNA-Skim

RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses sig-mers, a special type of k-mers)

Software (C++)

http://www.csbio.unc.edu/rs/

Variant calling

ChimeRScope

Fusion transcript prediction using gene k-mers profiles of the RNA-seq paired-end reads

Software (Java)

https://github.com/ChimeRScope/ChimeRScope/wiki

FastGT

Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique k-mers

Software (C)

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

Phy-Mer

Reference-independent mitochondrial haplogroup classifier from NGS data (k-mer based)

Software (Python)

https://github.com/danielnavarrogomez/phy-mer

LAVA

Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (k-mer based)

Software (C)

http://lava.csail.mit.edu/

MICADo

Detection of mutations in targeted third-generation NGS data (can distinguish patients’ specific mutations; algorithm uses k-mers and is based on colored de Bruijn graphs)

Software (Python)

http://github.com/cbib/MICADo

General mapper

Minimap

Lightweight and fast read mapper and read overlap detector (uses the concept of “minimazers”, a special type of k-mers)

Software (C)

https://github.com/lh3/minimap

Assembly

De novo genome assembly

MHAP

Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash

Software (Java)

https://github.com/marbl/MHAP

Miniasm

Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)

Software (C)

https://github.com/lh3/miniasm

LINKS

Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)

Software (Perl)

https://github.com/warrenlr/LINKS/

Read clustering

afcluster

Clustering of reads from different genes and different species based on k-mer counts

Software (C++)

https://github.com/luscinius/afcluster

QCluster

Clustering of reads with alignment-free measures (k-mer based) and quality values

Software (C++)

http://www.dei.unipd.it/~ciompin/main/qcluster.html

Reads error correction

Lighter

Correction of sequencing errors in raw, whole genome sequencing reads (k-mer based)

Software (C++)

https://github.com/mourisl/Lighter

QuorUM

Error corrector for Illumina reads using k-mers

Software (C++)

https://github.com/gmarcais/Quorum

Trowel

Software (C++)

https://sourceforge.net/projects/trowel-ec/

Metagenomics

Assembly-free phylogenomics

AAF

Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (k-mer based)

Software (Python)

https://github.com/fanhuan/AAF

kSNP v3

Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on k-mer analysis)

Software (C)

https://sourceforge.net/projects/ksnp/files/

NGS-MC

Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2 S under different Markov chain models (using k-words)

R package

http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html

Species identification/taxonomic profiling

CLARK

Taxonomic classification of metagenomic reads to known bacterial genomes using k-mer search and LCA assignment

Software (C++)

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

FOCUS

Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)

Web service Software (Python)

http://edwards.sdsu.edu/FOCUS/

GSM

Estimation of abundances of microbial genomes in metagenomic samples (k-mer based)

Software (Go)

https://github.com/pdtrang/GSM

Mash

Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)

Software (C++)

https://github.com/marbl/mash

Kraken

Taxonomic assignment in metagenome analysis by exact k-mer search; LCA assignment of short reads based on a comprehensive sequence database

Software (C++)

https://ccb.jhu.edu/software/kraken/

LMAT

Assignment of taxonomic labels to reads by k-mers searches in precomputed database

Software (C++/Python)

https://sourceforge.net/projects/lmat/

stringMLST

k-mer-based tool for MLST directly from the genome sequencing reads

Software (Python)

http://jordan.biology.gatech.edu/page/software/stringMLST

Taxonomer

k-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples

Web service

http://taxonomer.iobio.io/

Other

d2-tools

Word-based (k-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads

Software (Python/R)

https://code.google.com/p/d2-tools/

VirHostMatcher

Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)

Software (C++)

https://github.com/jessieren/VirHostMatcher

MetaFast

Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray–Curtis dissimilarity measure

Software (Java)

https://github.com/ctlab/metafast

miREM: an expectation-maximization approach for prioritizing miRNAs associated with gene-set

Abhimanyu Singh — Thu, 16 Aug 2018 04:48:07 -0500

From list of genes... ... to microRNAs

Using established miRNA-interaction databases, miREM combines hypergeometric and expectation-maximization algorithms to sieve through your genetic data for microRNA signatures

https://bioinfo-csi.nus.edu.sg/mirem2/

Address of the bookmark: https://bioinfo-csi.nus.edu.sg/mirem2/

HiTE: a fast and accurate dynamic boundary adjustment approach for full-length Transposable Elements detection and annotation in Genome Assemblies

LEGE — Sat, 20 Sep 2025 09:34:04 -0500

HiTE is a Python software that uses a dynamic boundary adjustment approach to detect and annotate full-length Transposable Elements in Genome Assemblies. In comparison to other tools, HiTE demonstrates superior performance in detecting a greater number of full-length TEs.

panHiTE

We have developed panHiTE, a comprehensive and accurate pipeline for TE detection in large-scale population genomes. It has been successfully applied to hundreds of plant population genomes, demonstrating its effectiveness and scalability.

For detailed instructions, please refer to the panHiTE tutorial.

Address of the bookmark: https://github.com/CSU-KangHu/HiTE

ONT assembly and Illumina polishing pipeline

Jit — Thu, 23 Nov 2017 10:13:42 -0600

This pipeline performs the following steps:

Assembly of nanopore reads using Canu.
Polish canu contigs using racon (optional).
Map a paired-end Illumina dataset onto the contigs obtained in the previous steps using BWA mem.
Perform correction of contigs using pilon and the Illumina dataset.

Address of the bookmark: https://github.com/nanoporetech/ont-assembly-polish

dnaPipeTE: de-novo assembly & annotation Pipeline for Transposable Elements

Rahul Nayak — Sat, 02 Dec 2017 18:25:44 -0600

dnaPipeTE (for de-novo assembly & annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (< 1X).

dnaPipeTE is developped by Clément Goubert, Laurent Modolo and the TREEP team of the LBBE: http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en
You can find the original publication in GBE here: https://academic.oup.com/gbe/article/7/4/1192/533768

output examples of quantification and TE landscape (relative age) produced by dnaPipeTE

Address of the bookmark: https://github.com/clemgoub/dnaPipeTE