BOL: Related items

Jaeger : an accurate and fast deep-learning tool to detect bacteriophage sequences

LEGE — Sun, 31 Aug 2025 06:30:16 -0500

Jaeger is a tool that utilizes homology-free machine learning to identify phage genome sequences that are hidden within metagenomes. It is capable of detecting both phages and prophages within metagenomic assemblies.

Address of the bookmark: https://github.com/MGXlab/Jaeger

CroCo: A program to detect potential cross contaminations in HTS assembled transcriptomes using expression level quantification

Jit — Mon, 07 Jan 2019 18:17:44 -0600

CroCo is a program to detect cross contamination events in assembled transcriptomes using sequencing reads to determine the true origin of every transcripts.
Such cross contaminations can be expected if several RNA-Seq experiments were prepared during the same period at the same lab, or by the same people, or if they were processed or sequenced by the same sequencing service facility.
Our approach first determines a subset of transcripts that are suspiciously similar across samples using a pairwise BLAST procedure. CroCo then combine all transcriptomes into a metatranscriptome and quantifies the "expression level" of all transcripts successively using every sample read data (e.g. several species sequenced by the same lab for a particular study) while allowing read multi-mappings.
Several mapping tools implemented in CroCo can be used to estimate expression level (default is RapMap).
This information is then used to categorize each transcript in the following 5 categories :

clean: the transcript origin is from the focal sample.

cross contamination: the transcript origin is from an alien sample of the same experiment.

dubious: expression levels are too close between focal and alien samples to determine the true origin of the transcript.

low coverage: expression levels are too low in all samples, thus hampering our procedure (which relies on differential expression) to confidently assign it to any category.

over expressed: expression levels are very high in at least 3 samples and CroCo will not try to categorize it. Indeed, such a pattern does not correspond to expectations for cross contaminations, but often reflect highly conserved genes such as ribosomal gene, or external contamination shared by several samples (e.g. Escherichia coli contaminations).

Address of the bookmark: https://gitlab.mbb.univ-montp2.fr/mbb/CroCo

chromeister: An ultra fast, heuristic approach to detect conserved signals in extremely large pairwise genome comparisons.

Jit — Thu, 03 Feb 2022 04:01:55 -0600

chromeister: An ultra fast, heuristic approach to detect conserved signals in extremely large pairwise genome comparisons.

USAGE:

-query: sequence A in fasta format
-db: sequence B in fasta format
-out: output matrix
-kmer Integer: k>1 (default 32) Use 32 for chromosomes and genomes and 16 for small bacteria
-diffuse Integer: z>0 (default 4) Use 4 for everything - if using large plant genomes you can try using 1
-dimension Size of the output matrix and plot. Integer: d>0 (default 1000) Use 1000 for everything that is not full genome size, where 2000 is recommended

Address of the bookmark: https://github.com/estebanpw/chromeister

LAST

Archana Malhotra — Wed, 09 Mar 2016 14:27:01 -0600

LAST can:

Handle big sequence data, e.g:
- Compare two vertebrate genomes
- Align billions of DNA reads to a genome
Indicate the reliability of each aligned column.
Use sequence quality data properly.
Compare DNA to proteins, with frameshifts.
Compare PSSMs to sequences
Calculate the likelihood of chance similarities between random sequences.
Do split and spliced alignment.
Train alignment parameters for unusual kinds of sequence (e.g. nanopore).

Address of the bookmark: http://last.cbrc.jp/

SISRS: Site Identification from Short Read Sequences

Abhimanyu Singh — Wed, 28 Nov 2018 08:56:03 -0600

Next-gen sequence data such as Illumina HiSeq reads. Data must be sorted into folders by taxon (e.g. species or genus). Paired reads in fastq format must be specified by _R1 and _R2 in the (otherwise identical) filenames. Paired and unpaired reads must have a fastq file extension.

Address of the bookmark: https://github.com/rachelss/SISRS

KAST

Neel — Wed, 23 Feb 2022 08:28:36 -0600

Perform Alignment-free k-tuple frequency comparisons from sequences. This can be in the form of two input files (e.g. a reference and a query) or a single file for pairwise comparisons to be made.

Address of the bookmark: https://github.com/martinjvickers/KAST

SMASH: An alignment-free tool to find and visualise rearrangements between pairs of DNA sequences

Jit — Thu, 21 Dec 2017 08:26:57 -0600

SMASH is a completely alignment-free method to find and visualise rearrangements between pairs of DNA sequences. The detection is based on relative compression, namely using a FCM, also known as Markov model, of high context order (typically 20). The method has been approached with a tool (also called SMASH). For visualization, SMASH outputs a SVG image, with an ideogram output architecture, where the patterns are represented with several HSV values (only value varies). The following image, illustrating the information maps between human and chimpanzee for the several chromosomes, depicts an example:

Address of the bookmark: https://github.com/pratas/smash

S-plot2: creates an interactive, two-dimensional heatmap of sequences

Jit — Fri, 28 Sep 2018 05:36:19 -0500

S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.

http://www.putonti-lab.com/uploads/4/5/3/0/45307835/s-plot2_tutorial.pdf

http://journals.sagepub.com/doi/pdf/10.1177/1176934318797354

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot

DECIPHER; a software toolset for deciphering and managing biological sequences efficiently using the R

BioStar — Sun, 09 Dec 2018 19:06:17 -0600

DECIPHER is a software toolset that can be used for deciphering and managing biological sequences efficiently using the R programming language. The R package is distributed as platform independent source code under the GPL version 3 license. Some functionality of the program is accessible online through web tools.

Address of the bookmark: http://www2.decipher.codes/

UPhO: Scripts for homology and orthology assessment from genomic sequences.

BioStar — Mon, 14 Jan 2019 10:36:42 -0600

UPhO finds orthologs with and without inparalogs from input gene family trees. Refer to the Documentation.pdf for more detailed explanations on its usage, installation and dependencies. Type UPhO.py -h for help.

The only input requierement for UPhO is a tree (or trees) in Newick format in which the leaves are named with a species idenfifier, a field separator, and sequence identifier. By default, the field separator is the character "|" but custom delimiters can be defined. Examples of trees to test UPhO are provided in the TestData folder.

Address of the bookmark: https://github.com/ballesterus/UPhO