16S rRNS gēna vairāku un divu mainīgo reģionu mikrobioma rekonstrukcijas algoritmu iegūto rezultātu salīdzināšana
Date
2022
Authors
Liepa, Edgars
Journal Title
Journal ISSN
Volume Title
Publisher
Latvijas Universitāte
Abstract
16S rRNS marķiergēna sekvencēšana ir viena no plašāk izmantotākajām un lētākajām mikrobioma taksonomiskā profila rekonstruēšanas metodēm. Taču, īso sekvencēšanas nolasījumu dēļ, visi gēna mainīgie reģioni nevar tikt nolasīti vienlaicīgi, bet tos nolasot atsevišķi zūd katra atsevišķā reģiona sasaiste ar pārējiem molekulas reģioniem kā arī par katru var tikt iegūts atšķirīgs datu apjoms, kas ierobežo kopējo mikrobioma kopas profilēšanas izšķirtspēju. Šajā darbā tiek aplūkota varbūtiskās rekonstrukcijas algoritma pieeja, kura izmanto vairāku 16S rRNS gēna reģionu noleases datus un maksimālās iespējamības varbūtisko algoritmu mikrobioma taksonomiskā profila rekonstrukcijai. Izmantojot Short MUltiple Reads Framework (SMURF) python implementācijas “Q2-SIDLE” 16S rRNS gēna reģionu rekonstrukcijas algoritmu un Latvijas Biomedicīnas pētījumu un studiju centrā pieejamās Atlantijas lašu (Salmo salar) zarnu mikrobioma paraugu 16S rRNS gēna sešu variablo reģionu gēnu sekvences nolasījumus, tika veikta mikrobioma rekonstrukcija un noteikta tajā esošo mikroorganismu taksonomiskā piederība un sadalījums. Iegūtie rezultāti tika salīdzināti ar 16S rRNS divu variablo reģionu mikrobioma rekonstrukcijas rezultātiem, kas iegūts no tiem pašiem mikrobioma datiem. Iegūtie rezultāti apstiprināja hipotēzi, ka vairāku mainīgo reģionu apvienošana samazina efektu, kad atsevišķi reģioni viena mikroorganisma ietvaros tiek nolasīti dažādā daudzumā un ļauj noteikt precīzāku mikroorganismu taksonomisko piederību gan ģints, gan sugas līmenī.
Sequencing of the 16S rRNA marker gene is one of the most widely used and inexpensive methods for reconstructing the taxonomic profile of a microbiome. However, due to the short sequencing reads, different variable RNA regions within a single microorganism can’t be read simultaneously, but when RNA regions are read separately there is a loss of a linkage between rest of the gen regions and varying amounts of data is obtained from regions, which are limiting factors that limits the profiling resolution of the total microbiome set. In this work, the probabilistic reconstruction algorithm approach, which uses multiple regions of the 16S rRNA gene and the maximum likelihood probabilistic framework for reconstruction of the microbiome taxonomic profile, is considered. Python implementation “Q2-SIDLE” of 16S rRNA gene region reconstruction algorithm short MUltiple Reads Framework (SMURF) and 16S rRNA gene variable region gene sequences of the Atlantic salmon (Salmo salar) intestinal microbiome samples available at the Latvian Biomedical Research and Study Center was used for microbial reconstruction, taxonomic assignment and to determine distribution of microorganisms. The obtained results were compared with the reconstruction data of two variable regions 16S rRNA from the same samples of Atlantic salmon. The results confirmed the hypothesis that the combination of several variable regions reduces the effect that the regions within a single microorganism are read in different numbers and allows to determine more accurately the taxonomic affiliation of microorganisms at both genus and species level.
Sequencing of the 16S rRNA marker gene is one of the most widely used and inexpensive methods for reconstructing the taxonomic profile of a microbiome. However, due to the short sequencing reads, different variable RNA regions within a single microorganism can’t be read simultaneously, but when RNA regions are read separately there is a loss of a linkage between rest of the gen regions and varying amounts of data is obtained from regions, which are limiting factors that limits the profiling resolution of the total microbiome set. In this work, the probabilistic reconstruction algorithm approach, which uses multiple regions of the 16S rRNA gene and the maximum likelihood probabilistic framework for reconstruction of the microbiome taxonomic profile, is considered. Python implementation “Q2-SIDLE” of 16S rRNA gene region reconstruction algorithm short MUltiple Reads Framework (SMURF) and 16S rRNA gene variable region gene sequences of the Atlantic salmon (Salmo salar) intestinal microbiome samples available at the Latvian Biomedical Research and Study Center was used for microbial reconstruction, taxonomic assignment and to determine distribution of microorganisms. The obtained results were compared with the reconstruction data of two variable regions 16S rRNA from the same samples of Atlantic salmon. The results confirmed the hypothesis that the combination of several variable regions reduces the effect that the regions within a single microorganism are read in different numbers and allows to determine more accurately the taxonomic affiliation of microorganisms at both genus and species level.
Description
Keywords
Datorzinātne , mikrobioms , 16s rRNS , taksonomija , filoģenētika , mikrobioloģija