J. Comput. Biol. - IDBA-MTP: A Hybrid Metatranscriptomic Assembler Based on Protein Information.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ general(901) number(790) one(736) }
{ method(1557) propos(1049) approach(1037) }
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{ high(1669) rate(1365) level(1280) }
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{ framework(1458) process(801) describ(734) }
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{ data(3963) clinic(1234) research(1004) }
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{ age(1611) year(1155) adult(843) }
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{ structur(1116) can(940) graph(676) }
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{ can(774) often(719) complex(702) }
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{ network(2748) neural(1063) input(814) }
{ data(1714) softwar(1251) tool(1186) }
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Resumo

Metatranscriptomic analysis provides information on how a microbial community reacts to environmental changes. Using next-generation sequencing (NGS) technology, biologists can study the microbe community by sampling short reads from a mixture of mRNAs (metatranscriptomic data). As most microbial genome sequences are unknown, it would seem that de novo assembly of the mRNAs is needed. However, NGS reads are short and mRNAs share many similar regions and differ tremendously in abundance levels, making de novo assembly challenging. The existing assembler, IDBA-MT, designed specifically for the assembly of metatranscriptomic data and performs well only on high-expressed mRNAs. This article introduces IDBA-MTP, which adopts a novel approach to metatranscriptomic assembly that makes use of the fact that there is a database of millions of known protein sequences associated with mRNAs. How to effectively use the protein information is nontrivial given the size of the database and given that different mRNAs might lead to proteins with similar functions (because different amino acids might have similar characteristics). IDBA-MTP employs a similarity measure between mRNAs and protein sequences, dynamic programming techniques, and seed-and-extend heuristics to tackle the problem effectively and efficiently. Experimental results show that IDBA-MTP outperforms existing assemblers by reconstructing 14% more mRNAs.

Resumo Limpo

metatranscriptom analysi provid inform microbi communiti react environment chang use nextgener sequenc ngs technolog biologist can studi microb communiti sampl short read mixtur mrnas metatranscriptom data microbi genom sequenc unknown seem de novo assembl mrnas need howev ngs read short mrnas share mani similar region differ tremend abund level make de novo assembl challeng exist assembl idbamt design specif assembl metatranscriptom data perform well highexpress mrnas articl introduc idbamtp adopt novel approach metatranscriptom assembl make use fact databas million known protein sequenc associ mrnas effect use protein inform nontrivi given size databas given differ mrnas might lead protein similar function differ amino acid might similar characterist idbamtp employ similar measur mrnas protein sequenc dynam program techniqu seedandextend heurist tackl problem effect effici experiment result show idbamtp outperform exist assembl reconstruct mrnas

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