A new generation of sequencing technologies is revolutionizing molecular biology. Illumina's Solexa and Applied Biosystems' SOLiD generate gigabases of nucleotide sequence per week. However, a perceived limitation of these ultra-high-throughput technologies is their short read-lengths. De novo assembly of sequence reads generated by classical Sanger capillary sequencing is a mature field of research. Unfortunately, the existing sequence assembly programs were not effective for short sequence reads generated by Illumina and SOLiD platforms. Early studies suggested that, in principle, sequence reads as short as 20-30 nucleotides could be used to generate useful assemblies of both prokaryotic and eukaryotic genome sequences, albeit containing many gaps. The early feasibility studies and proofs of principle inspired several bioinformatics research groups to implement new algorithms as freely available software tools specifically aimed at assembling reads of 30-50 nucleotides in length. This has led to the generation of several draft genome sequences based exclusively on short sequence Illumina sequence reads, recently culminating in the assembly of the 2.25-Gb genome of the giant panda from Illumina sequence reads with an average length of just 52 nucleotides. As well as reviewing recent developments in the field, we discuss some practical aspects such as data filtering and submission of assembly data to public repositories.

BACKGROUND: Next-generation sequencing technologies allow researchers to obtain millions of sequence reads in a single experiment. One important use of the technology is the sequencing of small non-coding regulatory RNAs and the identification of the genomic locales from which they originate. Currently, there is a paucity of methods for finding small RNA generative locales. RESULTS: We describe and implement an algorithm that can determine small RNA generative locales from high-throughput sequencing data. The algorithm creates a network, or graph, of the small RNAs by creating links between them depending on their proximity on the target genome. For each of the sub-networks in the resulting graph the clustering coefficient, a measure of the interconnectedness of the subnetwork, is used to identify the generative locales. We test the algorithm over a wide range of parameters using RFAM sequences as positive controls and demonstrate that the algorithm has good sensitivity and specificity in a range of Arabidopsis and mouse small RNA sequence sets and that the locales it generates are robust to differences in the choice of parameters. CONCLUSIONS: NiBLS is a fast, reliable and sensitive method for determining small RNA locales in high-throughput sequence data that is generally applicable to all classes of small RNA.

UNLABELLED: Recent developments in high-throughput sequencing technologies have generated considerable demand for tools to analyse large datasets of small RNA sequences. Here, we describe a suite of web-based tools for processing plant small RNA datasets. Our tools can be used to identify micro RNAs and their targets, compare expression levels in sRNA loci, and find putative trans-acting siRNA loci. AVAILABILITY: The tools are freely available for use at http://srna-tools.cmp.uea.ac.uk.

Abstract Summary: We report AtPRIMER, an application that automates the discovery of new polymorphic markers between ecotypes of Arabidopsis thaliana. On specifying two ecotypes and the genomic region of interest, the script retrieves all corresponding single nucleotide polymorphisms (SNPs) and generates CAPS and/or dCAPS PCR primer sequences. We show that AtPRIMER accurately found specific polymorphic markers for our linkage mapping project. AtPRIMER will therefore be useful for efficient marker development with high density and specificity. Availability: This PERL/CGI program is available for non-commercial users at http://www.AtPRIMER.tsl.ac.uk. The web-based version is available for public use at the same URL. Contact: [email protected]

UNLABELLED: We report a novel protein domain-G8-which contains five repeated beta-strand pairs and is present in some disease-related proteins such as PKHD1, KIAA1199, TMEM2 as well as other uncharacterized proteins. Most G8-containing proteins are predicted to be membrane-integral or secreted. The G8 domain may be involved in extracellular ligand binding and catalysis. It has been reported that mis-sense mutations in the two G8 domains of human PKHD1 protein resulted in a less stable protein and are associated with autosomal-recessive polycystic kidney disease, indicating the importance of the domain structure. G8 is also present in the N-terminus of some non-syndromic hearing loss disease-related proteins such as KIAA1109 and TMEM2. Discovery of G8 domain will be important for the research of the structure/function of related proteins and beneficial for the development of novel therapeutics. CONTACT: [email protected]

BACKGROUND: We wished to compare two databases based on sequence similarity: one that aims to be comprehensive in its coverage of known sequences, and one that specialises in a relatively small subset of known sequences. One of the motivations behind this study was quality control. Pfam is a comprehensive collection of alignments and hidden Markov models representing families of proteins and domains. MEROPS is a catalogue and classification of enzymes with proteolytic activity (peptidases or proteases). These secondary databases are used by researchers worldwide, yet their contents are not peer reviewed. Therefore, we hoped that a systematic comparison of the contents of Pfam and MEROPS would highlight missing members and false-positives leading to improvements in quality of both databases. An additional reason for carrying out this study was to explore the extent of consensus in the definition of a protein family. RESULTS: About half (89 out of 174) of the peptidase families in MEROPS overlapped single Pfam families. A further 32 MEROPS families overlapped multiple Pfam families. Where possible, new Pfam families were built to represent most of the MEROPS families that did not overlap Pfam. When comparing the numbers of sequences found in the overlap between a MEROPS family and its corresponding Pfam family, in most cases the overlap was substantial (52 pairs of MEROPS and Pfam families had an intersection size of greater than 75% of the union) but there were some differences in the sets of sequences included in the MEROPS families versus the overlapping Pfam families. CONCLUSIONS: A number of the discrepancies between MEROPS families and their corresponding Pfam families arose from differences in the aims and philosophies of the two databases. Examination of some of the discrepancies highlighted additional members of families, which have subsequently been added in both Pfam and MEROPS. This has led to improvements in the quality of both databases. Overall there was a great deal of consensus between the databases in definitions of a protein family.