BOL: Related items

Reference-free prediction of rearrangement breakpoint reads

Neel — Thu, 08 Mar 2018 05:05:25 -0600

lideSort-BPR ( b reak p oint r eads) is based on a fast algorithm for all-against-all comparisons of short reads and theoretical analyses of the number of neighboring reads. When applied to a dataset with a sequencing depth of 100×, it finds ∼88% of the breakpoints correctly with no false-positive reads. Moreover, evaluation on a real prostate cancer dataset shows that the proposed method predicts more fusion transcripts correctly than previous approaches, and yet produces fewer false-positive reads. To our knowledge, this is the first method to detect breakpoint reads without using a reference genome.

https://github.com/ewijaya/slidesort-bpr

Address of the bookmark: https://code.google.com/archive/p/slidesort-bpr/

Kaiju

Jit — Mon, 27 Jun 2016 11:23:04 -0500

Kaiju is a program for the taxonomic classification of metagenomic high-throughput sequencing reads. Each read is directly assigned to a taxon within the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences.

By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database nr used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.

Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.

Kaiju is described in Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257 (open access).

Address of the bookmark: http://kaiju.binf.ku.dk/

Rules for Pango Lineage !

Abhi — Tue, 14 Dec 2021 04:40:26 -0600

All the rules to classify a Lineage !

https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

Address of the bookmark: https://www.pango.network/the-pango-nomenclature-system/statement-of-nomenclature-rules/

CLARK: Fast, accurate and versatile sequence classification system

Jit — Sat, 15 Feb 2020 01:49:01 -0600

CLARK, a method based on a supervised sequence classification using discriminative k-mers. Considering two distinct specific classification problems (see the article for details), namely (1) the taxonomic classification of metagenomic reads to known bacterial genomes, and (2) the assignment of BAC clones and transcript to chromosome arms/centromeres (in the absence of a finished assembly for the reference genome), CLARK outperforms in classification speed and precision the best state-of-the-art methods.

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

Address of the bookmark: http://clark.cs.ucr.edu/Spaced/

Tiara: deep learning-based classification system for eukaryotic sequences

Rahul Nayak — Mon, 14 Mar 2022 23:02:11 -0500

With a large number of metagenomic datasets becoming available, eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step toward a better understanding of eukaryotic diversity.

Address of the bookmark: https://academic.oup.com/bioinformatics/article/38/2/344/6375939

Metabuli 분리 improves metagenomic read classification

Abhi — Sat, 03 Jun 2023 20:15:04 -0500

Metabuli 분리 improves metagenomic read classification through metamers, DNA-AA k-mers, to be sensitive and specific, recovering 99% and 98% of DNA or AA classifiers.

Metabuli is metagenomic classifier that jointly analyze both DNA and amino acid (AA) sequences. DNA-based classifiers can make specific classifications, exploiting point mutations to distinguish close taxa. AA-based classifiers have higher sensitivity in detecting homology between query and reference sequences, leverageing higher conservation of AA sequences. Metabuli combines the information of both sequence types using a novel k-mer structure, metamer, to enable both specific and sensitive characterization of metagenomic samples. In addition, it can classify reads against a database of any size as long as it fits in the hard disk.

Address of the bookmark: https://github.com/steineggerlab/Metabuli

BioGPS

Anjana — Mon, 23 May 2016 03:15:46 -0500

A free extensible and customizable gene annotation portal, a complete resource for learning about gene and protein function.

http://biogps.org/#goto=welcome

Address of the bookmark: http://biogps.org/#goto=welcome

JPred4: A Protein Secondary Structure Prediction Server

Poonam Mahapatra — Fri, 29 Dec 2017 16:14:28 -0600

JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction.

Address of the bookmark: http://www.compbio.dundee.ac.uk/jpred4/

GenomeThreader: Gene Prediction Software

Neel — Wed, 03 Oct 2018 15:34:08 -0500

GenomeThreader is a software tool to compute gene structure predictions. The gene structure predictions are calculated using a similarity-based approach where additional cDNA/EST and/or protein sequences are used to predict gene structures via spliced alignments. GenomeThreader was motivated by disabling limitations in GeneSeqer, a popular gene prediction program which is widely used for plant genome annotation.

Address of the bookmark: http://genomethreader.org/

GLEAN: an unsupervised learning system to integrate disparate sources of gene structure evidence

Poonam Mahapatra — Sat, 02 Jun 2018 07:38:33 -0500

GLEAN is an unsupervised learning system to integrate disparate sources of gene structure evidence (gene model predictions, EST/protein genomic sequence alignments, SAGE/peptide tags, etc) to produce a consensus gene prediction, without prior training.

Address of the bookmark: https://sourceforge.net/projects/glean-gene/