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 |
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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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Project description:BackgroundIn recent years several different fields, such as ecology, medicine and microbiology, have experienced an unprecedented development due to the possibility of direct sequencing of microbioimic samples. Among problems that researchers in the field have to deal with, taxonomic classification of metagenomic reads is one of the most challenging. State of the art methods classify single reads with almost 100% precision. However, very often, the performance in terms of recall falls at about 50%. As a consequence, state-of-the-art methods are indeed capable of correctly classify only half of the reads in the sample. How to achieve better performances in terms of overall quality of classification remains a largely unsolved problem.ResultsIn this paper we propose a method for metagenomics CLassification Improvement with Overlapping Reads (CLIOR), that exploits the information carried by the overlapping reads graph of the input read dataset to improve recall, f-measure, and the estimated abundance of species. In this work, we applied CLIOR on top of the classification produced by the classifier Clark-l. Experiments on simulated and synthetic metagenomes show that CLIOR can lead to substantial improvement of the recall rate, sometimes doubling it. On average, on simulated datasets, the increase of recall is paired with an higher precision too, while on synthetic datasets it comes at expenses of a small loss of precision. On experiments on real metagenomes CLIOR is able to assign many more reads while keeping the abundance ratios in line with previous studies.ConclusionsOur results showed that with CLIOR is possible to boost the recall of a state-of-the-art metagenomic classifier by inferring and/or correcting the assignment of reads with missing or erroneous labeling. CLIOR is not restricted to the reads classification algorithm used in our experiments, but it may be applied to other methods too. Finally, CLIOR does not need large computational resources, and it can be run on a laptop.
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