Metagenomic reads of an 18th century parchment
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB32319 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR3858142_1.fastq.gz | Fastqsanger.gz | |||
| ERR3858142_2.fastq.gz | Fastqsanger.gz | |||
| ERR3858143_1.fastq.gz | Fastqsanger.gz | |||
| ERR3858143_2.fastq.gz | Fastqsanger.gz | |||
| ERR3858144_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 9 |
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2021-07-26 | E-MTAB-9189 | biostudies-arrayexpress
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2021-07-26 | E-MTAB-9191 | biostudies-arrayexpress
Project description:MOTIVATION: Metagenomics is a recent field of biology that studies microbial communities by analyzing their genomic content directly sequenced from the environment. A metagenomic dataset consists of many short DNA or RNA fragments called reads. One interesting problem in metagenomic data analysis is the discovery of the taxonomic composition of a given dataset. A simple method for this task, called the Lowest Common Ancestor (LCA), is employed in state-of-the-art computational tools for metagenomic data analysis of very short reads (about 100 bp). However LCA has two main drawbacks: it possibly assigns many reads to high taxonomic ranks and it discards a high number of reads. RESULTS: We present MTR, a new method for tackling these drawbacks using clustering at Multiple Taxonomic Ranks. Unlike LCA, which processes the reads one-by-one, MTR exploits information shared by reads. Specifically, MTR consists of two main phases. First, for each taxonomic rank, a collection of potential clusters of reads is generated, and each potential cluster is associated to a taxon at that rank. Next, a small number of clusters is selected at each rank using a combinatorial optimization algorithm. The effectiveness of the resulting method is tested on a large number of simulated and real-life metagenomes. Results of experiments show that MTR improves on LCA by discarding a significantly smaller number of reads and by assigning much more reads at lower taxonomic ranks. Moreover, MTR provides a more faithful taxonomic characterization of the metagenome population distribution. AVAILABILITY: Matlab and C++ source codes of the method available at http://cs.ru.nl/gori/software/MTR.tar.gz.
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Project description:Metagenomic sequencing projects yield numerous sequencing reads of a diverse range of uncultivated and mostly yet unknown microorganisms. In many cases, these sequencing reads cannot be assembled into longer contigs. Thus, gene prediction tools that were originally developed for whole-genome analysis are not suitable for processing metagenomes. Orphelia is a program for predicting genes in short DNA sequences that is available through a web server application (http://orphelia.gobics.de). Orphelia utilizes prediction models that were created with machine learning techniques on the basis of a wide range of annotated genomes. In contrast to other methods for metagenomic gene prediction, Orphelia has fragment length-specific prediction models for the two most popular sequencing techniques in metagenomics, chain termination sequencing and pyrosequencing. These models ensure highly specific gene predictions.
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Project description:Parchment as a reservoir of ancient DNA for next generation sequencing
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Project description:UnlabelledBackgroundThe metagenomics approach allows the simultaneous sequencing of all genomes in an environmental sample. This results in high complexity datasets, where in addition to repeats and sequencing errors, the number of genomes and their abundance ratios are unknown. Recently developed next-generation sequencing (NGS) technologies significantly improve the sequencing efficiency and cost. On the other hand, they result in shorter reads, which makes the separation of reads from different species harder. Among the existing computational tools for metagenomic analysis, there are similarity-based methods that use reference databases to align reads and composition-based methods that use composition patterns (i.e., frequencies of short words or l-mers) to cluster reads. Similarity-based methods are unable to classify reads from unknown species without close references (which constitute the majority of reads). Since composition patterns are preserved only in significantly large fragments, composition-based tools cannot be used for very short reads, which becomes a significant limitation with the development of NGS. A recently proposed algorithm, AbundanceBin, introduced another method that bins reads based on predicted abundances of the genomes sequenced. However, it does not separate reads from genomes of similar abundance levels.ResultsIn this work, we present a two-phase heuristic algorithm for separating short paired-end reads from different genomes in a metagenomic dataset. We use the observation that most of the l-mers belong to unique genomes when l is sufficiently large. The first phase of the algorithm results in clusters of l-mers each of which belongs to one genome. During the second phase, clusters are merged based on l-mer repeat information. These final clusters are used to assign reads. The algorithm could handle very short reads and sequencing errors. It is initially designed for genomes with similar abundance levels and then extended to handle arbitrary abundance ratios. The software can be download for free at http://www.cs.ucr.edu/∼tanaseio/toss.htm.ConclusionsOur tests on a large number of simulated metagenomic datasets concerning species at various phylogenetic distances demonstrate that genomes can be separated if the number of common repeats is smaller than the number of genome-specific repeats. For such genomes, our method can separate NGS reads with a high precision and sensitivity.
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Project description:The literature of environmental microbiology broadly discusses issues associated with microbial hazards in archives, but these publications are mainly devoted to paper documents. There are few articles on historical parchment documents, which used to be very important for the development of literature and the art of writing. These studies present a broad spectrum of methods for the assessment of biodeterioration hazards of the parchment document in question. They are based on both conventional microbiological methods and advanced techniques of molecular biology. Here, a qualitative analysis was conducted, based on genetic identification of bacteria and fungi present on the document as well as denaturing gradient gel electrophoresis profiling and examining the destructive potential of isolated microbes. Moreover, the study involved a quantitative and qualitative microbiological assessment of the indoor air in the room where the parchment was kept. The microbes with the highest destructive potential that were isolated from the investigated item were Bacillus cereus and Acinetobacter lwoffii bacteria and Penicillium chrysogenum,Chaetomium globosum, and Trichoderma longibrachiatum fungi. The presence of the B. cereuss train was particularly interesting since, under appropriate conditions, it leads to complete parchment degradation within several days.
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