22 research outputs found
Exploring Pandora's Box: potential and pitfalls of low coverage genome surveys for evolutionary biology
High throughput sequencing technologies are revolutionizing genetic research. With this ‘‘rise of the machines’’, genomic sequences can be obtained even for unknown genomes within a short time and for reasonable costs. This has enabled evolutionary biologists studying genetically unexplored species to identify molecular markers or genomic regions of interest (e.g. micro- and minisatellites, mitochondrial and nuclear genes) by sequencing only a fraction of the genome. However, when using such datasets from non-model species, it is possible that DNA from non-target contaminant species such as bacteria, viruses, fungi, or other eukaryotic organisms may complicate the interpretation of the results. In this study we
analysed 14 genomic pyrosequencing libraries of aquatic non-model taxa from four major evolutionary lineages. We
quantified the amount of suitable micro- and minisatellites, mitochondrial genomes, known nuclear genes and transposable elements and searched for contamination from various sources using bioinformatic approaches. Our results show that in all sequence libraries with estimated coverage of about 0.02–25%, many appropriate micro- and minisatellites, mitochondrial gene sequences and nuclear genes from different KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways could be identified and characterized. These can serve as markers for phylogenetic and population genetic analyses. A central finding of our study is that several genomic libraries suffered from different biases owing to non-target DNA or mobile elements. In
particular, viruses, bacteria or eukaryote endosymbionts contributed significantly (up to 10%) to some of the libraries analysed. If not identified as such, genetic markers developed from high-throughput sequencing data for non-model organisms may bias evolutionary studies or fail completely in experimental tests. In conclusion, our study demonstrates the enormous potential of low-coverage genome survey sequences and suggests bioinformatic analysis workflows. The results also advise a more sophisticated filtering for problematic sequences and non-target genome sequences prior to developing markers
Exploring Pandora's Box
High throughput sequencing technologies are revolutionizing genetic research. With this "rise of the machines", genomic sequences can be obtained even for unknown genomes within a short time and for reasonable costs. This has enabled evolutionary biologists studying genetically unexplored species to identify molecular markers or genomic regions of interest (e.g. micro- and minisatellites, mitochondrial and nuclear genes) by sequencing only a fraction of the genome. However, when using such datasets from non-model species, it is possible that DNA from non-target contaminant species such as bacteria, viruses, fungi, or other eukaryotic organisms may complicate the interpretation of the results. In this study we analysed 14 genomic pyrosequencing libraries of aquatic non-model taxa from four major evolutionary lineages. We quantified the amount of suitable micro- and minisatellites, mitochondrial genomes, known nuclear genes and transposable elements and searched for contamination from various sources using bioinformatic approaches. Our results show that in all sequence libraries with estimated coverage of about 0.02–25%, many appropriate micro- and minisatellites, mitochondrial gene sequences and nuclear genes from different KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways could be identified and characterized. These can serve as markers for phylogenetic and population genetic analyses. A central finding of our study is that several genomic libraries suffered from different biases owing to non-target DNA or mobile elements. In particular, viruses, bacteria or eukaryote endosymbionts contributed significantly (up to 10%) to some of the libraries analysed. If not identified as such, genetic markers developed from high-throughput sequencing data for non-model organisms may bias evolutionary studies or fail completely in experimental tests. In conclusion, our study demonstrates the enormous potential of low-coverage genome survey sequences and suggests bioinformatic analysis workflows. The results also advise a more sophisticated filtering for problematic sequences and non-target genome sequences prior to developing markers
‘Isolated actions against non-Aryans are to be strictly discontinued’ the Hitler Youth and violence against Jews
Correlation between genomic library size (y-axis) and total length of mitochondrial genome recovered (x-axis).
<p>A significantly positive linear correlation (Pearson r = 0.6049, P = 0.0219) between the number of base pairs sequenced and the proportion of the mitochondrial genome recovered was found.</p
rRNA genes found in the different libraries.
<p>The total number of reads, the number of assembled contigs with coverage and the total unique rRNA gene bp are listed.</p
Hits of nuclear genes against KEGG BRITE Ontology database using the KAAS pipeline for the 16 genomic libraries.
<p>The number of hits is listed below the species name. Colours assigned according to the highest level of KEGG Orthology hierarchy (different organismal/cellular pathway groups/ecosystem processes).</p
Bacterial hits found in four genomic libraries.
<p>Hits for the bacterial species are displayed next to the chart pie for species with ≥4% of the hits. For the three pycnogonid species, Gammaproteobacteria are predominant, whereas for the vent limpet <i>Lepetodrilus</i> sp. nov. different bacterial groups were detected. The colours of the charts relate to the phyla/classes of Bacteria (see legend).</p
Workflow showing the methodological approach followed in this study.
<p>In this study we used a MySQL database (*) for storing the contigs. Other database formats are possible or reads can also be stored locally without a specific database.</p