BOL: Related items

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

LoRMA: A tool for correcting sequencing errors in long reads

Abhimanyu Singh — Thu, 06 Sep 2018 16:21:01 -0500

An error correction method that uses long reads only. The method consists of two phases: first, we use an iterative alignment-free correction method based on de Bruijn graphs with increasing length of k-mers, and second, the corrected reads are further polished using long-distance dependencies that are found using multiple alignments. According to our experiments, the proposed method is the most accurate one relying on long reads only for read sets with high coverage. Furthermore, when the coverage of the read set is at least 75×, the throughput of the new method is at least 20% higher.

conda install -c atgc-montpellier lorma

Address of the bookmark: https://gite.lirmm.fr/lorma/lorma-releases/wikis/home

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/

HASLR: a tool for rapid genome assembly of long sequencing reads

LEGE — Fri, 31 Jan 2020 05:50:15 -0600

HASLR is a tool for rapid genome assembly of long sequencing reads. HASLR is a hybrid tool which means it requires long reads generated by Third Generation Sequencing technologies (such as PacBio or Oxford Nanopore) together with Next Generation Sequencing reads (such as Illumina) from the same sample.

Address of the bookmark: https://github.com/vpc-ccg/haslr

QuasR: Quantification and annotation of short reads in R

Neel — Fri, 13 Aug 2021 07:44:05 -0500

The QuasR package (short for Quantify and annotate short reads in R) integrates the functionality of several R packages (such as IRanges (Lawrence et al. 2013) and Rsamtools) and external software (e.g. bowtie, through the Rbowtie package, and HISAT2, through the Rhisat2 package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single R script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html

cutadapt

Radha Agarkar — Fri, 13 May 2016 04:54:50 -0500

Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.

Cleaning your data in this way is often required: Reads from small-RNA sequencing contain the 3’ sequencing adapter because the read is longer than the molecule that is sequenced. Amplicon reads start with a primer sequence. Poly-A tails are useful for pulling out RNA from your sample, but often you don’t want them to be in your reads.

Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.

If you use cutadapt, please cite DOI:10.14806/ej.17.1.200 .

Address of the bookmark: https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart

Bpipe - a tool for running and managing bioinformatics pipelines

Radha Agarkar — Sat, 21 May 2016 22:42:16 -0500

Bpipe provides a platform for running big bioinformatics jobs that consist of a series of processing stages - known as 'pipelines'.

January 20th, 2016 - New! Bpipe 0.9.9 released!
Download latest, all
Documentation
Mailing List (Google Group)

Bpipe has been published in Bioinformatics! If you use Bpipe, please cite:

Sadedin S, Pope B & Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics

Address of the bookmark: http://docs.bpipe.org/

CoLoRMap: Correcting Long Reads by Mapping short reads

Jit — Mon, 20 Aug 2018 14:17:05 -0500

Second generation sequencing technologies paved the way to an exceptional increase in the number of sequenced genomes, both prokaryotic and eukaryotic. However, short reads are difficult to assemble and often lead to highly fragmented assemblies. The recent developments in long reads sequencing methods offer a promising way to address this issue. However, so far long reads are characterized by a high error rate, and assembling from long reads require a high depth of coverage. This motivates the development of hybrid approaches that leverage the high quality of short reads to correct errors in long reads.We introduce CoLoRMap, a hybrid method for correcting noisy long reads, such as the ones produced by PacBio sequencing technology, using high-quality Illumina paired-end reads mapped onto the long reads. Our algorithm is based on two novel ideas: using a classical shortest path algorithm to find a sequence of overlapping short reads that minimizes the edit score to a long read and extending corrected regions by local assembly of unmapped mates of mapped short reads. Our results on bacterial, fungal and insect data sets show that CoLoRMap compares well with existing hybrid correction methods.The source code of CoLoRMap is freely available for non-commercial use at https://github.com/sfu-compbio/colormap

ehaghshe@sfu.ca or cedric.chauve@sfu.ca

Address of the bookmark: https://github.com/sfu-compbio/colormap

Finding the Hidden Switches: The Story of KiNext and Protein Kinases

LEGE — Thu, 24 Sep 2026 02:51:28 -0500

Every newly sequenced genome contains thousands of proteins, but identifying what each protein does is a much harder task. Among these proteins are protein kinases, important molecular regulators that control processes such as cell growth, development, metabolism, stress responses, and signaling. Finding these kinases and determining which families they belong to can reveal important clues about how an organism functions and has evolved.

This is where KiNext comes into the picture. Introduced in a 2024 study published in BMC Bioinformatics, KiNext is a computational workflow designed to identify and classify protein kinases from predicted protein sequences. Instead of relying on a single search method, it brings together several approaches, including Hidden Markov Models, sequence alignment, phylogenetic analysis, and structural comparison.

The search begins with a simple question: does a protein contain the characteristics of a kinase? Protein kinases can change considerably during evolution, but important regions of their sequences often retain recognizable patterns. KiNext uses Hidden Markov Models, or HMMs, to detect these patterns. An HMM does not require a protein to be an exact match to a known kinase. Instead, it looks for a statistical sequence signature associated with kinase proteins, making it possible to detect more distant candidates.

Once potential kinases are identified, KiNext takes the analysis further. It distinguishes conventional eukaryotic protein kinases from atypical protein kinases and then attempts to classify them into different kinase groups and families. This distinction is important because simply identifying a protein as a kinase does not tell the complete story. Different kinase families can have very different evolutionary histories and biological functions.

The next stage brings evolution into the picture. KiNext can align kinase sequences and construct phylogenetic trees, allowing researchers to examine how newly identified proteins are related to previously characterized kinases. When sequence evidence alone is difficult to interpret, structural information can provide another clue. The workflow can incorporate AlphaFold-predicted structures and Foldseek-based structural comparisons to investigate whether an unusual protein resembles known kinase structures.

The researchers tested KiNext using two very different organisms: the Pacific oyster, Crassostrea gigas, and the green alga Ostreococcus tauri. In C. gigas, KiNext recovered previously reported kinases while identifying additional candidates. Structural analysis provided further evidence for many of the newly detected proteins. In O. tauri, the workflow similarly recovered most previously reported kinases and identified additional candidates while refining some of their classifications.

What makes KiNext particularly interesting is not just its ability to find kinases, but how the entire analysis is organized. The workflow uses Nextflow, allowing the different computational steps to be connected into a reproducible pipeline. Containers can also help manage software dependencies, making it easier to run the workflow across different computing environments.

This reproducibility becomes increasingly important as the number of available genomes continues to grow. A researcher studying one organism may be able to perform an analysis manually, but repeating the same process across hundreds or thousands of genomes quickly becomes impractical. A standardized workflow provides a way to perform the analysis consistently while keeping track of how the results were generated.

At its core, KiNext demonstrates a broader change taking place in modern genomics. Sequencing a genome provides an enormous amount of information, but the real scientific challenge begins afterward: understanding what all those sequences mean. Protein kinases are only one part of this larger puzzle, yet they are particularly important because they act as molecular switches throughout the cell.

By combining sequence profiles, evolutionary analysis, and structural evidence within a reproducible computational framework, KiNext provides researchers with a systematic way to uncover these molecular switches. Its real value lies not only in finding more kinases, but in making the process scalable, repeatable, and easier to apply to new genomes.

As genome sequencing continues to expand across the tree of life, tools such as KiNext can help turn enormous collections of protein sequences into meaningful biological stories—one kinase at a time.

What Junk DNA? It’s an Operating System

Rahul Agarwal — Mon, 19 Aug 2013 15:24:26 -0500

The report adds to growing experimental support for the idea that all that extra stuff in the human genes, once referred to as “junk DNA,” is more than functionless, space-filling material that happens to make up nearly 98% of the genome. The paper adds to a growing body of knowledge establishing a considerable role for this material in the regulation of gene expression and its potential role in human disease.

Address of the bookmark: http://www.genengnews.com/keywordsandtools/print/3/32115/