Improving the genotyping resolution of Cryptosporidium hominis subtype IbA10G2 using one step PCR-based amplicon sequencing.

Cryptosporidium hominis gp60 subtype IbA10G2 is a common cause of cryptosporidiosis. This subtype is responsible for many waterborne outbreaks as well as sporadic cases and is considered virulent and highly important in the epidemiology of cryptosporidiosis. Due to low heterogeneity within the genome of C. hominis it has been difficult to identify epidemiological markers with higher resolution than gp60. However, new markers are required in order to improve outbreak investigations and studies of the transmission dynamics of this clinically important subtype. Based on the whole genome sequences of 17 C. hominis isolates, we have identified several differential loci and developed a new sequence based typing panel with higher resolution than gp60. An amplicon sequencing method was also developed which is based on a one-step PCR which can be sequenced using a Next Generation Sequencing (NGS) platform. Such a system provides a rapid and high-throughput workflow. A panel of nine loci with 10 single nucleotide variants (SNV) was selected and evaluated using clinical IbA10G2 isolates from sporadic, cluster and outbreak associated cases. The specimens were separated into 10 different genetic profiles named sequence types (STs). All isolates within an outbreak or cluster belonged to the same ST, including several samples from the two large waterborne outbreaks which occurred in Sweden between 2010 and 2011 indicating that these outbreaks might be linked. The results demonstrate the methods suitability for improved genotyping of C. hominis IbA10G2.

InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic.

The InParanoid database (http://InParanoid.sbc.su.se) provides a user interface to orthologs inferred by the InParanoid algorithm. As there are now international efforts to curate and standardize complete proteomes, we have switched to using these resources rather than gathering and curating the proteomes ourselves. InParanoid release 8 is based on the 66 reference proteomes that the 'Quest for Orthologs' community has agreed on using, plus 207 additional proteomes from the UniProt complete proteomes--in total 273 species. These represent 246 eukaryotes, 20 bacteria and seven archaea. Compared to the previous release, this increases the number of species by 173% and the number of pairwise species comparisons by 650%. In turn, the number of ortholog groups has increased by 423%. We present the contents and usages of InParanoid 8, and a detailed analysis of how the proteome content has changed since the previous release.

Avoiding pitfalls in gene (co)expression meta-analysis.

Differential gene expression analysis between healthy and diseased groups is a widely used approach to understand the molecular underpinnings of disease. A wide variety of experimental and bioinformatics techniques are available for this type of analysis, yet their impact on the reliability of the results has not been systematically studied. We performed a large scale comparative analysis of clinical expression data, using several background corrections and differential expression metrics. The agreement between studies was analyzed for study pairs of same cancer type, of different cancer types, and between cancer and non-cancer studies. We also replicated the analysis using differential coexpression. We found that agreement of differential expression is primarily dictated by the microarray platform, while differential coexpression requires large sample sizes. Two studies using different differential expression metrics may show no agreement, even if they agree strongly using the same metric. Our analysis provides practical recommendations for gene (co)expression analysis.

Quality criteria for finding genes with high mRNA-protein expression correlation and coexpression correlation.

mRNA expression is widely used as a proxy for protein expression. However, their true relation is not known and two genes with the same mRNA levels might have different abundances of respective proteins. A related question is whether the coexpression of mRNA for gene pairs is reflected by the corresponding protein pairs. We examined the mRNA-protein correlation for both expression and coexpression. This analysis yielded insights into the relationship between mRNA and protein abundance, and allowed us to identify subsets of greater mRNA-protein coherence. The correlation between mRNA and protein was low for both expression and coexpression, 0.12 and 0.06 respectively. However, applying the best-performing quality measure, high-quality subsets reached a Spearman correlation of 0.31 for expression, 0.34 for coexpression and 0.49 for coexpression when restricted to functionally coupled genes. Our methodology can thus identify subsets for which the mRNA levels are expected to be the strongest correlated with protein levels.

Network-based Identification of novel cancer genes.

Genes involved in cancer susceptibility and progression can serve as templates for searching protein networks for novel cancer genes. To this end, we introduce a general network searching method, MaxLink, and apply it to find and rank cancer gene candidates by their connectivity to known cancer genes. Using a comprehensive protein interaction network, we searched for genes connected to known cancer genes. First, we compiled a new set of 812 genes involved in cancer, more than twice the number in the Cancer Gene Census. Their network neighbors were then extracted. This candidate list was refined by selecting genes with unexpectedly high levels of connectivity to cancer genes and without previous association to cancer. This produced a list of 1891 new cancer candidates with up to 55 connections to known cancer genes. We validated our method by cross-validation, Gene Ontology term bias, and differential expression in cancer versus normal tissue. An example novel cancer gene candidate is presented with detailed analysis of the local network and neighbor annotation. Our study provides a ranked list of high priority targets for further studies in cancer research. Supplemental material is included.

InParanoid 7: new algorithms and tools for eukaryotic orthology analysis.

The InParanoid project gathers proteomes of completely sequenced eukaryotic species plus Escherichia coli and calculates pairwise ortholog relationships among them. The new release 7.0 of the database has grown by an order of magnitude over the previous version and now includes 100 species and their collective 1.3 million proteins organized into 42.7 million pairwise ortholog groups. The InParanoid algorithm itself has been revised and is now both more specific and sensitive. Based on results from our recent benchmarking of low-complexity filters in homology assignment, a two-pass BLAST approach was developed that makes use of high-precision compositional score matrix adjustment, but avoids the alignment truncation that sometimes follows. We have also updated the InParanoid web site (http://InParanoid.sbc.su.se). Several features have been added, the response times have been improved and the site now sports a new, clearer look. As the number of ortholog databases has grown, it has become difficult to compare among these resources due to a lack of standardized source data and incompatible representations of ortholog relationships. To facilitate data exchange and comparisons among ortholog databases, we have developed and are making available two XML schemas: SeqXML for the input sequences and OrthoXML for the output ortholog clusters.

InParanoid 6: eukaryotic ortholog clusters with inparalogs.

The InParanoid eukaryotic ortholog database (http://InParanoid.sbc.su.se/) has been updated to version 6 and is now based on 35 species. We collected all available 'complete' eukaryotic proteomes and Escherichia coli, and calculated ortholog groups for all 595 species pairs using the InParanoid program. This resulted in 2 642 187 pairwise ortholog groups in total. The orthology-based species relations are presented in an orthophylogram. InParanoid clusters contain one or more orthologs from each of the two species. Multiple orthologs in the same species, i.e. inparalogs, result from gene duplications after the species divergence. A new InParanoid website has been developed which is optimized for speed both for users and for updating the system. The XML output format has been improved for efficient processing of the InParanoid ortholog clusters.