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      Assessing De Novo transcriptome assembly metrics for consistency and utility

      research-article
      1 , 2 , 2 ,
      BMC Genomics
      BioMed Central

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          Abstract

          Background

          Transcriptome sequencing and assembly represent a great resource for the study of non-model species, and many metrics have been used to evaluate and compare these assemblies. Unfortunately, it is still unclear which of these metrics accurately reflect assembly quality.

          Results

          We simulated sequencing transcripts of Drosophila melanogaster. By assembling these simulated reads using both a “perfect” and a modern transcriptome assembler while varying read length and sequencing depth, we evaluated quality metrics to determine whether they 1) revealed perfect assemblies to be of higher quality, and 2) revealed perfect assemblies to be more complete as data quantity increased.

          Several commonly used metrics were not consistent with these expectations, including average contig coverage and length, though they became consistent when singletons were included in the analysis. We found several annotation-based metrics to be consistent and informative, including contig reciprocal best hit count and contig unique annotation count. Finally, we evaluated a number of novel metrics such as reverse annotation count, contig collapse factor, and the ortholog hit ratio, discovering that each assess assembly quality in unique ways.

          Conclusions

          Although much attention has been given to transcriptome assembly, little research has focused on determining how best to evaluate assemblies, particularly in light of the variety of options available for read length and sequencing depth. Our results provide an important review of these metrics and give researchers tools to produce the highest quality transcriptome assemblies.

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          Most cited references26

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          Performance comparison of benchtop high-throughput sequencing platforms.

          Three benchtop high-throughput sequencing instruments are now available. The 454 GS Junior (Roche), MiSeq (Illumina) and Ion Torrent PGM (Life Technologies) are laser-printer sized and offer modest set-up and running costs. Each instrument can generate data required for a draft bacterial genome sequence in days, making them attractive for identifying and characterizing pathogens in the clinical setting. We compared the performance of these instruments by sequencing an isolate of Escherichia coli O104:H4, which caused an outbreak of food poisoning in Germany in 2011. The MiSeq had the highest throughput per run (1.6 Gb/run, 60 Mb/h) and lowest error rates. The 454 GS Junior generated the longest reads (up to 600 bases) and most contiguous assemblies but had the lowest throughput (70 Mb/run, 9 Mb/h). Run in 100-bp mode, the Ion Torrent PGM had the highest throughput (80–100 Mb/h). Unlike the MiSeq, the Ion Torrent PGM and 454 GS Junior both produced homopolymer-associated indel errors (1.5 and 0.38 errors per 100 bases, respectively).
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            Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing.

            We present a de novo assembly of a eukaryote transcriptome using 454 pyrosequencing data. The Glanville fritillary butterfly (Melitaea cinxia; Lepidoptera: Nymphalidae) is a prominent species in population biology but had no previous genomic data. Sequencing runs using two normalized complementary DNA collections from a genetically diverse pool of larvae, pupae, and adults yielded 608,053 expressed sequence tags (mean length = 110 nucleotides), which assembled into 48,354 contigs (sets of overlapping DNA segments) and 59,943 singletons. BLAST comparisons confirmed the accuracy of the sequencing and assembly, and indicated the presence of c. 9000 unique genes, along with > 6000 additional microarray-confirmed unannotated contigs. Average depth of coverage was 6.5-fold for the longest 4800 contigs (348-2849 bp in length), sufficient for detecting large numbers of single nucleotide polymorphisms. Oligonucleotide microarray probes designed from the assembled sequences showed highly repeatable hybridization intensity and revealed biological differences among individuals. We conclude that 454 sequencing, when performed to provide sufficient coverage depth, allows de novo transcriptome assembly and a fast, cost-effective, and reliable method for development of functional genomic tools for nonmodel species. This development narrows the gap between approaches based on model organisms with rich genetic resources vs. species that are most tractable for ecological and evolutionary studies.
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              Sequencing and de novo analysis of a coral larval transcriptome using 454 GSFlx

              Background New methods are needed for genomic-scale analysis of emerging model organisms that exemplify important biological questions but lack fully sequenced genomes. For example, there is an urgent need to understand the potential for corals to adapt to climate change, but few molecular resources are available for studying these processes in reef-building corals. To facilitate genomics studies in corals and other non-model systems, we describe methods for transcriptome sequencing using 454, as well as strategies for assembling a useful catalog of genes from the output. We have applied these methods to sequence the transcriptome of planulae larvae from the coral Acropora millepora. Results More than 600,000 reads produced in a single 454 sequencing run were assembled into ~40,000 contigs with five-fold average sequencing coverage. Based on sequence similarity with known proteins, these analyses identified ~11,000 different genes expressed in a range of conditions including thermal stress and settlement induction. Assembled sequences were annotated with gene names, conserved domains, and Gene Ontology terms. Targeted searches using these annotations identified the majority of genes associated with essential metabolic pathways and conserved signaling pathways, as well as novel candidate genes for stress-related processes. Comparisons with the genome of the anemone Nematostella vectensis revealed ~8,500 pairs of orthologs and ~100 candidate coral-specific genes. More than 30,000 SNPs were detected in the coral sequences, and a subset of these validated by re-sequencing. Conclusion The methods described here for deep sequencing of the transcriptome should be widely applicable to generate catalogs of genes and genetic markers in emerging model organisms. Our data provide the most comprehensive sequence resource currently available for reef-building corals, and include an extensive collection of potential genetic markers for association and population connectivity studies. The characterization of the larval transcriptome for this widely-studied coral will enable research into the biological processes underlying stress responses in corals and evolutionary adaptation to global climate change.
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                Author and article information

                Contributors
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2013
                9 July 2013
                : 14
                : 465
                Affiliations
                [1 ]Center for Genome Research and Biocomputing, Oregon State University, Corvallis, OR 97333, USA
                [2 ]Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
                Article
                1471-2164-14-465
                10.1186/1471-2164-14-465
                3733778
                23837739
                399fb2e6-56fd-4a83-af3f-da8fc71b5034
                Copyright © 2013 O’Neil and Emrich; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 8 August 2012
                : 21 June 2013
                Categories
                Research Article

                Genetics
                Genetics

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