BOL: Related items

RAMI: a tool for identification and characterization of phylogenetic clusters in microbial communities

Jit — Mon, 07 Aug 2017 18:49:27 -0500

RAMI, which clusters related nodes in a phylogenetic tree based on the patristic distance. RAMI also produces indices of cluster properties and other indices used in population and community studies on-the-fly.

Availability: RAMI is licensed under GNU GPL and can be run or downloaded from http://www.acgt.se/online.html.

Address of the bookmark: https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btp051

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

bacLIFE: an automated genome mining tool for identification of lifestyle associated genes

BioStar — Fri, 15 Mar 2024 04:59:14 -0500

bacLIFE is a streamlined computational workflow that annotates bacterial genomes and performs large-scale comparative genomics to predict bacterial lifestyles and to pinpoint candidate genes, denominated lifestyle-associated genes (LAGs), and biosynthetic gene clusters associated with each lifestyle detected. This whole process is divided into different modules:

Clustering module Predicts, clusters and annotates the genes of every input genome
Lifestyle prediction Employs a machine learning model to forecast bacterial lifestyle or other specified metadata
Analitical module (Shiny app) Results from the previous modules are embedded in a user-friendly interface for comprehensive and interactive comparative genomics.

You can find the complete wiki here [https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki]

Address of the bookmark: https://github.com/Carrion-lab/bacLIFE

GRIDSS: the Genomic Rearrangement IDentification Software Suite

Rahul Nayak — Sun, 17 May 2020 10:27:44 -0500

GRIDSS is a module software suite containing tools useful for the detection of genomic rearrangements. GRIDSS includes a genome-wide break-end assembler, as well as a structural variation caller for Illumina sequencing data. GRIDSS calls variants based on alignment-guided positional de Bruijn graph genome-wide break-end assembly, split read, and read pair evidence.

Address of the bookmark: https://github.com/PapenfussLab/gridss

1mb long DNA with Nanopore technology

Jit — Tue, 19 Dec 2017 18:49:28 -0600

The first continuous DNA read of more than a million bases (>1Mb) has been achieved, using Oxford Nanopore sequencing technology. Congratulations to Martin Smith and collaborators! Read more: http://bit.ly/2j5TNCO

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

Read Simulators

Abhi — Fri, 30 Sep 2022 06:48:18 -0500

Short Read Simulators

With the popularity of next-generation sequencing (NGS) technologies, many NGS read simulators have been developed. Currently, many of the popular short read simulators are designed to simulate reads mimicking many Illumina, 454 and SOLiD platforms. Listed below are some popular short read simulators. Links to their publications are provided as well.

Long Read Simulators

With the advancements in sequencing technologies, scientists have shown an increasing interest in using third-generation sequencing (TGS) technologies. Currently, many of the popular long read simulators are designed to simulate reads mimicking the two main TGS technologies; (1) Pacific Biosciences (PacBio) and (2) Oxford Nanopore (ONT). Listed below are some of the popular and recently introduced PacBio and ONT simulators. Links to their publications are provided as well.

PacBio Simulators

ONT Simulators

proovread : large-scale high-accuracy PacBio correction through iterative short read consensus

Jit — Fri, 05 Jan 2018 04:12:20 -0600

proovread : large-scale high-accuracy PacBio correction through iterative short read consensus

outperforms PacBioToCA/LSC in terms of accuracy and contiguity/sensitivity (http://dx.doi.org/10.1093/bioinformatics/btu392)
is easy to install/run/configure
supports various types of dat
- HiSeq/MiSeq (100-500bp)
- Unitigs
- 454, ...

proovread maps high coverage data to pacbio reads (bwa mem, blasr, daligner) in multiple iterations.

Address of the bookmark: https://github.com/BioInf-Wuerzburg/proovread

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

SimLoRD: A read simulator for third generation sequencing reads

Aaryan Lokwani — Wed, 22 Aug 2018 10:40:27 -0500

SimLoRD is a read simulator for third generation sequencing reads and is currently focused on the Pacific Biosciences SMRT error model.

Reads are simulated from both strands of a provided or randomly generated reference sequence.

The reference can be read from a FASTA file or randomly generated with a given GC content. It can consist of several chromosomes, whose structure is respected when drawing reads. (Simulation of genome rearrangements may be incorporated at a later stage.)
The read lengths can be determined in four ways: drawing from a log-normal distribution (typical for genomic DNA), sampling from an existing FASTQ file (typical for RNA), sampling from a a text file with integers (RNA), or using a fixed length
Quality values and number of passes depend on fragment length.
Provided subread error probabilities are modified according to number of passes
Outputs reads in FASTQ format and alignments in SAM format

Address of the bookmark: https://bitbucket.org/genomeinformatics/simlord/