Unique Molecular Identifiers reveal a novel sequencing artefact with implications for RNA-Seq based gene expression analysis.

Attaching Unique Molecular Identifiers (UMI) to RNA molecules in the first step of sequencing library preparation establishes a distinct identity for each input molecule. This makes it possible to eliminate the effects of PCR amplification bias, which is particularly important where many PCR cycles are required, for example, in single cell studies. After PCR, molecules sharing a UMI are assumed to be derived from the same input molecule. In our single cell RNA-Seq studies of Physcomitrella patens, we discovered that reads sharing a UMI, and therefore presumed to be derived from the same mRNA molecule, frequently map to different, but closely spaced locations. This behaviour occurs in all such libraries that we have produced, and in multiple other UMI-containing RNA-Seq data sets in the public domain. This apparent paradox, that reads of identical origin map to distinct genomic coordinates may be partially explained by PCR stutter, which is often seen in low-entropy templates and those containing simple tandem repeats. In the absence of UMI this artefact is undetectable. We show that the common assumption that sequence reads having different mapping coordinates are derived from different starting molecules does not hold. Unless taken into account, this artefact is likely to result in over-estimation of certain transcript abundances, depending on the counting method employed.

A Whole Genome Assembly of the Horn Fly, Haematobia irritans, and Prediction of Genes with Roles in Metabolism and Sex Determination.

Haematobia irritans, commonly known as the horn fly, is a globally distributed blood-feeding pest of cattle that is responsible for significant economic losses to cattle producers. Chemical insecticides are the primary means for controlling this pest but problems with insecticide resistance have become common in the horn fly. To provide a foundation for identification of genomic loci for insecticide resistance and for discovery of new control technology, we report the sequencing, assembly, and annotation of the horn fly genome. The assembled genome is 1.14 Gb, comprising 76,616 scaffolds with N50 scaffold length of 23 Kb. Using RNA-Seq data, we have predicted 34,413 gene models of which 19,185 have been assigned functional annotations. Comparative genomics analysis with the Dipteran flies Musca domestica L., Drosophila melanogaster, and Lucilia cuprina, show that the horn fly is most closely related to M. domestica, sharing 8,748 orthologous clusters followed by D. melanogaster and L. cuprina, sharing 7,582 and 7,490 orthologous clusters respectively. We also identified a gene locus for the sodium channel protein in which mutations have been previously reported that confers target site resistance to the most common class of pesticides used in fly control. Additionally, we identified 276 genomic loci encoding members of metabolic enzyme gene families such as cytochrome P450s, esterases and glutathione S-transferases, and several genes orthologous to sex determination pathway genes in other Dipteran species.

Legume information system (LegumeInfo.org): a key component of a set of federated data resources for the legume family.

Legume Information System (LIS), at http://legumeinfo.org, is a genomic data portal (GDP) for the legume family. LIS provides access to genetic and genomic information for major crop and model legumes. With more than two-dozen domesticated legume species, there are numerous specialists working on particular species, and also numerous GDPs for these species. LIS has been redesigned in the last three years both to better integrate data sets across the crop and model legumes, and to better accommodate specialized GDPs that serve particular legume species. To integrate data sets, LIS provides genome and map viewers, holds synteny mappings among all sequenced legume species and provides a set of gene families to allow traversal among orthologous and paralogous sequences across the legumes. To better accommodate other specialized GDPs, LIS uses open-source GMOD components where possible, and advocates use of common data templates, formats, schemas and interfaces so that data collected by one legume research community are accessible across all legume GDPs, through similar interfaces and using common APIs. This federated model for the legumes is managed as part of the 'Legume Federation' project (accessible via http://legumefederation.org), which can be thought of as an umbrella project encompassing LIS and other legume GDPs.