Matches in SemOpenAlex for { <https://semopenalex.org/work/W2331883429> ?p ?o ?g. }
- W2331883429 abstract "This work reports the development of GenSeed-HMM, a program that implements seed-driven progressive assembly, an approach to reconstruct specific sequences from unassembled data, starting from short nucleotide or protein seed sequences or profile Hidden Markov Models (HMM). The program can use any one of a number of sequence assemblers. Assembly is performed in multiple steps and relatively few reads are used in each cycle, consequently the program demands low computational resources. As a proof-of-concept and to demonstrate the power of HMM-driven progressive assemblies, GenSeed-HMM was applied to metagenomic datasets in the search for diverse ssDNA bacteriophages from the recently described Alpavirinae subfamily. Profile HMMs were built using Alpavirinae-specific regions from multiple sequence alignments using either the viral protein 1 (VP1) (major capsid protein) or VP4 (genome replication initiation protein). These profile HMMs were used by GenSeed-HMM (running Newbler assembler) as seeds to reconstruct viral genomes from sequencing datasets of human fecal samples. All contigs obtained were annotated and taxonomically classified using similarity searches and phylogenetic analyses. The most specific profile HMM seed enabled the reconstruction of 45 partial or complete Alpavirinae genomic sequences. A comparison with conventional (global) assembly of the same original dataset, using Newbler in a standalone execution, revealed that GenSeed-HMM outperformed global genomic assembly in several metrics employed. This approach is capable of detecting organisms that have not been used in the construction of the profile HMM, which opens up the possibility of diagnosing novel viruses, without previous specific information, constituting a de novo diagnosis. Additional applications include, but are not limited to, the specific assembly of extrachromosomal elements such as plastid and mitochondrial genomes from metagenomic data. Profile HMM seeds can also be used to reconstruct specific protein coding genes for gene diversity studies, and to determine all possible gene variants present in a metagenomic sample. Such surveys could be useful to detect the emergence of drug-resistance variants in sensitive environments such as hospitals and animal production facilities, where antibiotics are regularly used. Finally, GenSeed-HMM can be used as an adjunct for gap closure on assembly finishing projects, by using multiple contig ends as anchored seeds." @default.
- W2331883429 created "2016-06-24" @default.
- W2331883429 creator A5005624489 @default.
- W2331883429 creator A5007100888 @default.
- W2331883429 creator A5014704956 @default.
- W2331883429 creator A5032097589 @default.
- W2331883429 creator A5042594694 @default.
- W2331883429 creator A5044928960 @default.
- W2331883429 creator A5061974067 @default.
- W2331883429 creator A5070519214 @default.
- W2331883429 creator A5073701909 @default.
- W2331883429 creator A5080657470 @default.
- W2331883429 creator A5082053897 @default.
- W2331883429 creator A5090385107 @default.
- W2331883429 date "2016-03-04" @default.
- W2331883429 modified "2023-10-18" @default.
- W2331883429 title "GenSeed-HMM: A Tool for Progressive Assembly Using Profile HMMs as Seeds and its Application in Alpavirinae Viral Discovery from Metagenomic Data" @default.
- W2331883429 cites W1480926151 @default.
- W2331883429 cites W1963991917 @default.
- W2331883429 cites W1966822396 @default.
- W2331883429 cites W1966912927 @default.
- W2331883429 cites W1989341817 @default.
- W2331883429 cites W1993314801 @default.
- W2331883429 cites W1996423252 @default.
- W2331883429 cites W1998585277 @default.
- W2331883429 cites W2001792012 @default.
- W2331883429 cites W2002526888 @default.
- W2331883429 cites W2008087865 @default.
- W2331883429 cites W2017593822 @default.
- W2331883429 cites W2020301105 @default.
- W2331883429 cites W2025850587 @default.
- W2331883429 cites W2032230795 @default.
- W2331883429 cites W2035793410 @default.
- W2331883429 cites W2035832880 @default.
- W2331883429 cites W2042919980 @default.
- W2331883429 cites W2058336694 @default.
- W2331883429 cites W2059157309 @default.
- W2331883429 cites W2059880645 @default.
- W2331883429 cites W2079706758 @default.
- W2331883429 cites W2081319466 @default.
- W2331883429 cites W2084814249 @default.
- W2331883429 cites W2088161476 @default.
- W2331883429 cites W2090610796 @default.
- W2331883429 cites W2094819389 @default.
- W2331883429 cites W2105199251 @default.
- W2331883429 cites W2113172269 @default.
- W2331883429 cites W2114104680 @default.
- W2331883429 cites W2120772351 @default.
- W2331883429 cites W2124648649 @default.
- W2331883429 cites W2127106003 @default.
- W2331883429 cites W2129933858 @default.
- W2331883429 cites W2132926880 @default.
- W2331883429 cites W2137674681 @default.
- W2331883429 cites W2138122982 @default.
- W2331883429 cites W2138618895 @default.
- W2331883429 cites W2139134537 @default.
- W2331883429 cites W2141015882 @default.
- W2331883429 cites W2142678478 @default.
- W2331883429 cites W2144241990 @default.
- W2331883429 cites W2149949571 @default.
- W2331883429 cites W2155570949 @default.
- W2331883429 cites W2157624015 @default.
- W2331883429 cites W2157930702 @default.
- W2331883429 cites W2157975034 @default.
- W2331883429 cites W2160969485 @default.
- W2331883429 cites W2161251845 @default.
- W2331883429 cites W2163631211 @default.
- W2331883429 cites W2164461702 @default.
- W2331883429 cites W2166265186 @default.
- W2331883429 cites W2167391272 @default.
- W2331883429 cites W2168696662 @default.
- W2331883429 cites W2170551349 @default.
- W2331883429 cites W2504833775 @default.
- W2331883429 cites W991869846 @default.
- W2331883429 doi "https://doi.org/10.3389/fmicb.2016.00269" @default.
- W2331883429 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4777721" @default.
- W2331883429 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26973638" @default.
- W2331883429 hasPublicationYear "2016" @default.
- W2331883429 type Work @default.
- W2331883429 sameAs 2331883429 @default.
- W2331883429 citedByCount "22" @default.
- W2331883429 countsByYear W23318834292018 @default.
- W2331883429 countsByYear W23318834292019 @default.
- W2331883429 countsByYear W23318834292020 @default.
- W2331883429 countsByYear W23318834292021 @default.
- W2331883429 countsByYear W23318834292022 @default.
- W2331883429 countsByYear W23318834292023 @default.
- W2331883429 crossrefType "journal-article" @default.
- W2331883429 hasAuthorship W2331883429A5005624489 @default.
- W2331883429 hasAuthorship W2331883429A5007100888 @default.
- W2331883429 hasAuthorship W2331883429A5014704956 @default.
- W2331883429 hasAuthorship W2331883429A5032097589 @default.
- W2331883429 hasAuthorship W2331883429A5042594694 @default.
- W2331883429 hasAuthorship W2331883429A5044928960 @default.
- W2331883429 hasAuthorship W2331883429A5061974067 @default.
- W2331883429 hasAuthorship W2331883429A5070519214 @default.
- W2331883429 hasAuthorship W2331883429A5073701909 @default.
- W2331883429 hasAuthorship W2331883429A5080657470 @default.
- W2331883429 hasAuthorship W2331883429A5082053897 @default.
- W2331883429 hasAuthorship W2331883429A5090385107 @default.