Diana L. Kolbe

Infernal 1.0: inference of RNA alignments

Summary: infernal builds consensus RNA secondary structure profiles called covariance models (CMs), and uses them to search nucleic acid sequence databases for homologous RNAs, or to create new sequence- and structure-based multiple sequence alignments. Availability: Source code, documentation and benchmark downloadable from http://infernal.janelia.org. infernal is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X. Contact: nawrockie,kolbed,gro.imhh.ailenaj@sydde

Infernal 1.0

SUMMARY INFERNAL builds consensus RNA secondary structure profiles called covariance models (CMs), and uses them to search nucleic acid sequence databases for homologous RNAs, or to create new sequence- and structure-based multiple sequence alignments. AVAILABILITY Source code, documentation and benchmark downloadable from http://infernal.janelia.org. INFERNAL is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X.

Rfam: updates to the RNA families database.

Rfam is a collection of RNA sequence families, represented by multiple sequence alignments and covariance models (CMs). The primary aim of Rfam is to annotate new members of known RNA families on nucleotide sequences, particularly complete genomes, using sensitive BLAST filters in combination with CMs. A minority of families with a very broad taxonomic range (e.g. tRNA and rRNA) provide the majority of the sequence annotations, whilst the majority of Rfam families (e.g. snoRNAs and miRNAs) have a limited taxonomic range and provide a limited number of annotations. Recent improvements to the website, methodologies and data used by Rfam are discussed. Rfam is freely available on the Web at http://rfam.sanger.ac.uk/and http://rfam.janelia.org/.

Rfam: Wikipedia, clans and the “decimal” release

The Rfam database aims to catalogue non-coding RNAs through the use of sequence alignments and statistical profile models known as covariance models. In this contribution, we discuss the pros and cons of using the online encyclopedia, Wikipedia, as a source of community-derived annotation. We discuss the addition of groupings of related RNA families into clans and new developments to the website. Rfam is available on the Web at http://rfam.sanger.ac.uk.

Copy number variants are a common cause of non-syndromic hearing loss

BackgroundCopy number variants (CNVs) are a well-recognized cause of genetic disease; however, methods for their identification are often gene-specific, excluded as ‘routine’ in screens of genetically heterogeneous disorders, and not implemented in most next-generation sequencing pipelines. For this reason, the contribution of CNVs to non-syndromic hearing loss (NSHL) is most likely under-recognized. We aimed to incorporate a method for CNV identification as part of our standard analysis pipeline and to determine the contribution of CNVs to genetic hearing loss.MethodsWe used targeted genomic enrichment and massively parallel sequencing to isolate and sequence all exons of all genes known to cause NSHL. We completed testing on 686 patients with hearing loss with no exclusions based on type of hearing loss or any other clinical features. For analysis we used an integrated method for detection of single nucleotide changes, indels and CNVs. CNVs were identified using a previously published method that utilizes median read-depth ratios and a sliding-window approach.ResultsOf 686 patients tested, 15.2% (104) carried at least one CNV within a known deafness gene. Of the 38.9% (267) of individuals for whom we were able to determine a genetic cause of hearing loss, a CNV was implicated in 18.7% (50). We identified CNVs in 16 different genes including 7 genes for which no CNVs have been previously reported. CNVs of STRC were most common (73% of CNVs identified) followed by CNVs of OTOA (13% of CNVs identified).ConclusionCNVs are an important cause of NSHL and their detection must be included in comprehensive genetic testing for hearing loss.

TBC1D24 Mutation Causes Autosomal-Dominant Nonsyndromic Hearing Loss

Hereditary hearing loss is extremely heterogeneous. Over 70 genes have been identified to date, and with the advent of massively parallel sequencing, the pace of novel gene discovery has accelerated. In a family segregating progressive autosomal‐dominant nonsyndromic hearing loss (NSHL), we used OtoSCOPE® to exclude mutations in known deafness genes and then performed segregation mapping and whole‐exome sequencing to identify a unique variant, p.Ser178Leu, in TBC1D24 that segregates with the hearing loss phenotype. TBC1D24 encodes a GTPase‐activating protein expressed in the cochlea. Ser178 is highly conserved across vertebrates and its change is predicted to be damaging. Other variants in TBC1D24 have been associated with a panoply of clinical symptoms including autosomal recessive NSHL, syndromic hearing impairment associated with onychodystrophy, osteodystrophy, mental retardation, and seizures (DOORS syndrome), and a wide range of epileptic disorders.

High-Throughput Genetic Testing for Thrombotic Microangiopathies and C3 Glomerulopathies

The thrombotic microangiopathies (TMAs) and C3 glomerulopathies (C3Gs) include a spectrum of rare diseases such as atypical hemolytic uremic syndrome, thrombotic thrombocytopenic purpura, C3GN, and dense deposit disease, which share phenotypic similarities and underlying genetic commonalities. Variants in several genes contribute to the pathogenesis of these diseases, and identification of these variants may inform the diagnosis and treatment of affected patients. We have developed and validated a comprehensive genetic panel that screens all exons of all genes implicated in TMA and C3G. The closely integrated pipeline implemented includes targeted genomic enrichment, massively parallel sequencing, bioinformatic analysis, and a multidisciplinary conference to analyze identified variants in the context of each patients specific phenotype. Herein, we present our 1-year experience with this panel, during which time we studied 193 patients. We identified 17 novel and 74 rare variants, which we classified as pathogenic (11), likely pathogenic (12), and of uncertain significance (68). Compared with controls, patients with C3G had a higher frequency of rare and novel variants in C3 convertase (C3 and CFB) and complement regulator (CFH, CFI, CFHR5, and CD46) genes (P<0.05). In contrast, patients with TMA had an increase in rare and novel variants only in complement regulator genes (P<0.01), a distinction consistent with differing sites of complement dysregulation in these two diseases. In summary, we were able to provide a positive genetic diagnosis in 43% and 41% of patients carrying the clinical diagnosis of C3G and TMA, respectively.

Reducing the Cost of the Diagnostic Odyssey in Early Onset Epileptic Encephalopathies

Whole exome sequencing (WES) has revolutionized the way we think about and diagnose epileptic encephalopathies. Multiple recent review articles discuss the benefits of WES and suggest various algorithms to follow for determining the etiology of epileptic encephalopathies. Incorporation of WES in these algorithms is leading to the discovery of new genetic diagnoses of early onset epileptic encephalopathies (EOEEs) at a rapid rate; however, WES is not yet a universally utilized diagnostic tool. Clinical WES may be underutilized due to provider discomfort in ordering the test or perceived costliness. At our hospital WES is not routinely performed for patients with EOEE due to limited insurance reimbursement. In fact for any patient with noncommercial insurance (Medicaid) the institution does not allow sending out WES as this is not “established”/“proven to be highly useful and cost effective”/“approved test” in patients with epilepsy. Recently, we performed WES on four patients from three families and identified novel mutations in known epilepsy genes in all four cases. These patients had State Medicaid as their insurance carrier and were followed up for several years for EOEE while being worked up using the traditional/approved testing methods. Following a recently proposed diagnostic pathway, we analyzed the cost savings (US dollars) that could be accrued if WES was performed earlier in the diagnostic odyssey. This is the first publication that addresses the dollar cost of traditional testing in EOEE as performed in these four cases versus WES and the potential cost savings.

Local RNA structure alignment with incomplete sequence

Motivation: Accuracy of automated structural RNA alignment is improved by using models that consider not only primary sequence but also secondary structure information. However, current RNA structural alignment approaches tend to perform poorly on incomplete sequence fragments, such as single reads from metagenomic environmental surveys, because nucleotides that are expected to be base paired are missing. Results: We present a local RNA structural alignment algorithm, trCYK, for aligning and scoring incomplete sequences under a model using primary sequence conservation and secondary structure information when possible. The trCYK algorithm improves alignment accuracy and coverage of sequence fragments of structural RNAs in simulated metagenomic shotgun datasets. Availability: The source code for Infernal 1.0, which includes trCYK, is available at http://infernal.janelia.org Contact: kolbed@janelia.hhmi.org; eddys@janelia.hhmi.org Supplementary information: Supplementary data are available at Bioinformatics online.

Fast filtering for RNA homology search

Motivation: Homology search for RNAs can use secondary structure information to increase power by modeling base pairs, as in covariance models, but the resulting computational costs are high. Typical acceleration strategies rely on at least one filtering stage using sequence-only search. Results: Here we present the multi-segment CYK (MSCYK) filter, which implements a heuristic of ungapped structural alignment for RNA homology search. Compared to gapped alignment, this approximation has lower computation time requirements (O(N4) reduced to O(N3)), and space requirements (O(N3) reduced to O(N2)). A vector-parallel implementation of this method gives up to 100-fold speed-up; vector-parallel implementations of standard gapped alignment at two levels of precision give 3- and 6-fold speed-ups. These approaches are combined to create a filtering pipeline that scores RNA secondary structure at all stages, with results that are synergistic with existing methods. Availability: http://selab.janelia.org/publications.html#KolbeEddy11 Contact: eddys@janelia.hhmi.org