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Assessing De Novo transcriptome assembly metrics for consistency and utility.

O'Neil ST, Emrich SJ - BMC Genomics (2013)

Bottom Line: We simulated sequencing transcripts of Drosophila melanogaster.We found several annotation-based metrics to be consistent and informative, including contig reciprocal best hit count and contig unique annotation count.Our results provide an important review of these metrics and give researchers tools to produce the highest quality transcriptome assemblies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Genome Research and Biocomputing, Oregon State University,Corvallis, OR 97333, USA.

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.

Show MeSH
Possible contig collapse identified in TBLASTN search. During assembly, a paralogous gene family may be collapsed into a single representative contig. If paralogs are individually represented in a reference dataset, this collapse may be found by matching the related proteins against unigenes and assessing hit counts for target sequences.
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Figure 4: Possible contig collapse identified in TBLASTN search. During assembly, a paralogous gene family may be collapsed into a single representative contig. If paralogs are individually represented in a reference dataset, this collapse may be found by matching the related proteins against unigenes and assessing hit counts for target sequences.

Mentions: Reverse annotation of contigs also revealed those that were matched by more than one protein. These may indicate erroneous “collapse” of reads from similar transcripts into a single consensus sequence (Figure 4). To evaluate this type of error, which should be less prevalent in more accurate assemblies, we assigned each contig and singleton a “Collapse Factor” (CF), computed simply as the number of D. melanogaster or B. mori proteins having a best match to the sequence.


Assessing De Novo transcriptome assembly metrics for consistency and utility.

O'Neil ST, Emrich SJ - BMC Genomics (2013)

Possible contig collapse identified in TBLASTN search. During assembly, a paralogous gene family may be collapsed into a single representative contig. If paralogs are individually represented in a reference dataset, this collapse may be found by matching the related proteins against unigenes and assessing hit counts for target sequences.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3733778&req=5

Figure 4: Possible contig collapse identified in TBLASTN search. During assembly, a paralogous gene family may be collapsed into a single representative contig. If paralogs are individually represented in a reference dataset, this collapse may be found by matching the related proteins against unigenes and assessing hit counts for target sequences.
Mentions: Reverse annotation of contigs also revealed those that were matched by more than one protein. These may indicate erroneous “collapse” of reads from similar transcripts into a single consensus sequence (Figure 4). To evaluate this type of error, which should be less prevalent in more accurate assemblies, we assigned each contig and singleton a “Collapse Factor” (CF), computed simply as the number of D. melanogaster or B. mori proteins having a best match to the sequence.

Bottom Line: We simulated sequencing transcripts of Drosophila melanogaster.We found several annotation-based metrics to be consistent and informative, including contig reciprocal best hit count and contig unique annotation count.Our results provide an important review of these metrics and give researchers tools to produce the highest quality transcriptome assemblies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Genome Research and Biocomputing, Oregon State University,Corvallis, OR 97333, USA.

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.

Show MeSH