BOL: Related items

The Gene Detectives: How a New Computational Tool Is Helping Us Read the History Written in Our Genes

BioStar — Thu, 10 Sep 2026 21:23:42 -0500

Imagine opening an ancient family album with no names, dates, or captions. Some photographs are missing, others have been copied, and a few show people who look almost identical. Your challenge is to discover who is related to whom and reconstruct the family story. In evolutionary biology, scientists face a similar mystery when they study genes. Every genome contains clues about its past, but genes can be duplicated, lost, and changed over millions of years, making their history difficult to trace.

A recent study published in PLOS Computational Biology introduces REvolutionH-tl 2.0 (https://pypi.org/project/revolutionhtl/), a computational tool designed to help solve this mystery. The software analyzes protein sequences and identifies evolutionary relationships between genes. It can help researchers detect important events such as gene duplication, gene loss, and speciation, allowing them to reconstruct how genes evolved across different organisms.

What makes the tool especially interesting is its focus on both speed and interpretation. The researchers compared REvolutionH-tl 2.0 with several existing computational tools and reported that it achieved competitive accuracy while requiring less computational time in their tests. The tool also includes visualization features, helping researchers turn complicated genetic data into evolutionary stories that are easier to explore.

Genes are more than simple sequences of DNA; they are records of life’s long history. Each surviving gene carries traces of the changes that shaped it. By bringing together computational analysis and visualization, REvolutionH-tl 2.0 acts like a detective, helping scientists uncover these hidden stories. Ultimately, tools like this can improve our understanding of how genomes evolved and how genes acquired the roles they perform today.

More at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013017

Circleator

Jit — Sun, 25 Jun 2017 18:04:32 -0500

The Charm City Circleator--or Circleator for short--is a Perl-based visualization tool developed at the Institute for Genome Sciences in the University of Maryland's School of Medicine. Circleator produces circular plots of genome-associated data, like this one:

Common uses of the tool include:

Displaying the sequence and/or genes in a GenBank flat file.
Highlighting differences and/or similarities in gene content between related organisms.
Comparing SNPs and indels between closely-related strains or serovars.
Comparing gene expression values across multiple samples or timepoints.
Visualizing coverage plots of RNA-Seq read alignments.

Key Features

Circleator...

Builds on BioPerl and the input file formats that it supports, including:
- GenBank flat files, GFF, FASTA
Accepts a number of other commonly-used datatypes and file formats:
- BSR and TRF output, SAM/BAM files, VCF-encoded SNPs, tab-delimited files
Outputs publication-ready figures in the SVG (Scalable Vector Graphics) format.
Requires only a single configuration file whose layout mirrors that of the figure itself.
- Predefined configuration files and "track" types are supplied for common datasets.
- Advanced features allow limited analyses to be performed as a figure is drawn.
Includes an extensive set of regression tests.
Offers a prototype web-based GUI (under the "Ringmaster" project.)

https://github.com/jonathancrabtree/Circleator

Address of the bookmark: https://github.com/jonathancrabtree/Circleator

COPE: an accurate k-mer-based pair-end reads connection tool to facilitate genome assembly

Jit — Wed, 06 Dec 2017 02:08:14 -0600

An efficient tool called Connecting Overlapped Pair-End (COPE) reads, to connect overlapping pair-end reads using k-mer frequencies. We evaluated our tool on 30× simulated pair-end reads from Arabidopsis thaliana with 1% base error. COPE connected over 99% of reads with 98.8% accuracy, which is, respectively, 10 and 2% higher than the recently published tool FLASH. When COPE is applied to real reads for genome assembly, the resulting contigs are found to have fewer errors and give a 14-fold improvement in the N50 measurement when compared with the contigs produced using unconnected reads.

Address of the bookmark: ftp://ftp.genomics.org.cn/pub/cope

GPOPSIM: a simulation tool for whole-genome genetic data

Jit — Wed, 17 Jan 2018 03:47:46 -0600

GPOPSIM is a simulation tool for pedigree, phenotypes, and genomic data, with a variety of population and genome structures and trait genetic architectures. It provides flexible parameter settings for a wide discipline of users, especially can simulate multiple genetically correlated traits with desired genetic parameters and underlying genetic architectures.

Address of the bookmark: https://github.com/SCAU-AnimalGenetics/GPOPSIM

BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data

Jit — Thu, 31 May 2018 09:35:23 -0500

BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes. The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC. https://github.com/lh3/bfc

Address of the bookmark: https://github.com/lh3/bfc

SALSA: A tool to scaffold long read assemblies with Hi-C

Jit — Fri, 15 Jun 2018 04:01:15 -0500

This code is used to scaffold your assemblies using Hi-C data. This version implements some improvements in the original SALSA algorithm. If you want to use the old version, it can be found in the old_salsa branch. To use the latest version, first run the following commands: cd SALSA make To run the code, you will need Python 2.7, BOOST libraries and Networkx(version lower than 1.2). If you consider using this tool, please cite our publication which describes the methods used for scaffolding. Ghurye, J., Pop, M., Koren, S., Bickhart, D., & Chin, C. S. (2017). Scaffolding of long read assemblies using long range contact information. BMC genomics, 18(1), 527. Link Ghurye, J., Rhie, A., Walenz, B.P., Schmitt, A., Selvaraj, S., Pop, M., Phillippy, A.M. and Koren, S., 2018. Integrating Hi-C links with assembly graphs for chromosome-scale assembly. bioRxiv, p.261149 Link For any queries, please either ask on github issue page or send an email to Jay Ghurye (jayg@cs.umd.edu).

Address of the bookmark: https://github.com/machinegun/SALSA

FMLRC: a long-read error correction tool using the multi-string Burrows Wheeler Transform

Neel — Fri, 10 Aug 2018 13:29:28 -0500

FMLRC, or FM-index Long Read Corrector, is a tool for performing hybrid correction of long read sequencing using the BWT and FM-index of short-read sequencing data. Given a BWT of the short-read sequencing data, FMLRC will build an FM-index and use that as an implicit de Bruijn graph. Each long read is then corrected independently by identifying low frequency k-mers in the long read and replacing them with the closest matching high frequency k-mers in the implicit de Bruijn graph. In contrast to other de Bruijn graph based implementations, FMLRC is not restricted to a particular k-mer size and instead uses a two pass method with both a short "k-mer" and a longer "K-mer". This allows FMLRC to correct through low complexity regions that are computational difficult for short k-mers.

Address of the bookmark: https://github.com/holtjma/fmlrc

SeqMonk:A tool to visualise and analyse high throughput mapped sequence data

Jit — Tue, 11 Sep 2018 04:39:38 -0500

SeqMonk is a program to enable the visualisation and analysis of mapped sequence data. It was written for use with mapped next generation sequence data but can in theory be used for any dataset which can be expressed as a series of genomic positions. It's main features are:

Import of mapped data from mapped data (BAM/SAM/bowtie etc)
Creation of data groups for visualisation and analysis
Visualisation of mapped regions against an annotated genome.
Flexible quantitation of the mapped data to allow comparisons between data sets
Statistical analysis of data to find regions of interest
Creation of reports containing data and genome annotation

Address of the bookmark: http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/

ANItools web: a web tool for fast genome comparison within multiple bacterial strains

Jit — Wed, 14 Nov 2018 04:34:23 -0600

ANItools is a software package written by PERL scripts that can be run in a Linux/Unix system. If you want to compare bacterial genomes and calculate their average nucleotide identity (ANI), you could download and run this program directly. Or you could send us the genome sequence by email. Then we will do the analysis work for you.

https://academic.oup.com/database/article/doi/10.1093/database/baw084/2630454

Address of the bookmark: http://ani.mypathogen.cn/

Minipolish: A tool for Racon polishing of miniasm assemblies

BioStar — Tue, 03 Dec 2019 02:40:54 -0600

Miniasm is a great long-read assembly tool: straight-forward, effective and very fast. However, it does not include a polishing step, so its assemblies have a high error rate – they are essentially made of stitched-together pieces of long reads.

Racon is a great polishing tool that can be used to clean up assembly errors. It's also very fast and well suited for long-read data. However, it operates on FASTA files, not the GFA graphs that miniasm makes.

That's where Minipolish comes in. With a single command, it will use Racon to polish up a miniasm assembly, while keeping the assembly in graph form.

It also takes care of some of the other nuances of polishing a miniasm assembly:

Adding read depth information to contigs
Fixing sequence truncation that can occur in Racon
Adding circularising links to circular contigs if not already present (so they display better in Bandage)
'Rotating' circular contigs between polishing rounds to ensure clean circularisation

Address of the bookmark: https://github.com/rrwick/Minipolish