TY - JOUR

T1 - FastContext: A tool for identification of adapters and other sequence patterns in next generation sequencing (NGS) data

AU - Viesná, Е

AU - Fishman, V

N1 - Viesná, E. FastContext: A tool for identification of adapters and other sequence patterns in next generation sequencing (NGS) data / E. Viesná, V. Fishman // Vavilov Journal of Genetics and Breeding. – 2022. – Vol. 26, No. 8. – P. 806-809. This work was supported by Russian Science Foundation, grant No. 22-14-00247. High-throughoutput computations required for FastContext testing were performed using the Collective usage center of the Institute of Cytology and Genetics SB RAS, 121031800061-7 (Mechanisms of genetic control of development, physiological processes and behavior in animals).

PY - 2022/12

Y1 - 2022/12

N2 - The development of next generation sequencing (NGS) methods has created the need for detailed analysis and control of each protocol step. NGS library preparation protocols may include steps with incorporation of various service sequences, such as sequencing adapters, primers, sample-, cell-, and molecule-specific barcodes. Despite a fairly high level of current knowledge, during the protocol development process researches often have to deal with various kinds of unexpected experiment outcomes, which result either from lack of information, lack of knowledge, or defects in reagent manufacturing. Detection and analysis of service sequences, their distribution and linkage may provide important information for protocol optimization. Here we introduce FastContext, a tool designed to analyze NGS read structure, based on sequence features found in reads, and their relative position in the read. The algorithm is able to create human readable read structures with user-specified patterns, to calculate counts and percentage of every read structure. Despite the simplicity of the algorithm, FastContext may be useful in read structure analysis and, as a result, can help better understand molecular processes that take place at different stages of NGS library preparation. The project is open-source software, distributed under GNU GPL v3, entirely written in the programming language Python, and based on well-maintained packages and commonly used data formats. Thus, it is cross-platform, may be patched or upgraded by the user if necessary. The FastContext package is available at the Python Package Index (https://pypi.org/project/FastContext), the source code is available at GitHub (https://github.com/regnveig/FastContext).

AB - The development of next generation sequencing (NGS) methods has created the need for detailed analysis and control of each protocol step. NGS library preparation protocols may include steps with incorporation of various service sequences, such as sequencing adapters, primers, sample-, cell-, and molecule-specific barcodes. Despite a fairly high level of current knowledge, during the protocol development process researches often have to deal with various kinds of unexpected experiment outcomes, which result either from lack of information, lack of knowledge, or defects in reagent manufacturing. Detection and analysis of service sequences, their distribution and linkage may provide important information for protocol optimization. Here we introduce FastContext, a tool designed to analyze NGS read structure, based on sequence features found in reads, and their relative position in the read. The algorithm is able to create human readable read structures with user-specified patterns, to calculate counts and percentage of every read structure. Despite the simplicity of the algorithm, FastContext may be useful in read structure analysis and, as a result, can help better understand molecular processes that take place at different stages of NGS library preparation. The project is open-source software, distributed under GNU GPL v3, entirely written in the programming language Python, and based on well-maintained packages and commonly used data formats. Thus, it is cross-platform, may be patched or upgraded by the user if necessary. The FastContext package is available at the Python Package Index (https://pypi.org/project/FastContext), the source code is available at GitHub (https://github.com/regnveig/FastContext).

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85152203703&origin=inward&txGid=3bdf37708dfcbd82de3da036656c71f8

UR - https://elibrary.ru/item.asp?id=50061124

UR - https://www.mendeley.com/catalogue/8c556453-7f55-3cf3-ac48-feadd4b1274e/

U2 - 10.18699/VJGB-22-97

DO - 10.18699/VJGB-22-97

M3 - Article

C2 - 36694721

VL - 26

SP - 806

EP - 809

JO - Вавиловский журнал генетики и селекции

JF - Вавиловский журнал генетики и селекции

SN - 2500-0462

IS - 8

M1 - 10

ER -