BackgroundSugarcane is an important global food crop and energy resource. To facilitate the sugarcane improvement program, genome and gene information are important for studying traits at the molecular level. Most currently available transcriptome data for sugarcane were generated using second-generation sequencing platforms, which provide short reads. The de novo assembled transcripts from these data are limited in length, and hence may be incomplete and inaccurate, especially for long RNAs.MethodsWe generated a transcriptome dataset of leaf tissue from a commercial Thai sugarcane cultivar Khon Kaen 3 (KK3) using PacBio RS II single-molecule long-read sequencing by the Iso-Seq method. Short-read RNA-Seq data were generated from the same RNA sample using the Ion Proton platform for reducing base calling errors.ResultsA total of 119,339 error-corrected transcripts were generated with the N50 length of 3,611 bp, which is on average longer than any previously reported sugarcane transcriptome dataset. 110,253 sequences (92.4%) contain an open reading frame (ORF) of at least 300 bp long with ORF N50 of 1,416 bp. The mean lengths of 5′ and 3′ untranslated regions in 73,795 sequences with complete ORFs are 1,249 and 1,187 bp, respectively. 4,774 transcripts are putatively novel full-length transcripts which do not match with a previous Iso-Seq study of sugarcane. We annotated the functions of 68,962 putative full-length transcripts with at least 90% coverage when compared with homologous protein coding sequences in other plants.DiscussionThe new catalog of transcripts will be useful for genome annotation, identification of splicing variants, SNP identification, and other research pertaining to the sugarcane improvement program. The putatively novel transcripts suggest unique features of KK3, although more data from different tissues and stages of development are needed to establish a reference transcriptome of this cultivar.

The task of calculating similarities between strings held by different organisations without revealing these strings is an increasingly important problem in areas such as health informatics, national censuses, genomics, and fraud detection. Most existing privacy-preserving string matching approaches are either based on comparing sets of encoded characters allowing only exact matching of encoded strings, or they are aimed at long genomics sequences that have a small alphabet. The set-based privacy-preserving similarity functions that are commonly used to compare name and address strings in the context of privacy-preserving record linkage do not take the positions of sub-strings into account. As a result, two very different strings can potentially be considered as a match leading to wrongly linked records. Furthermore, existing set-based techniques cannot identify the length of the longest common sub-string across two strings. In this paper, we propose two new approaches for accurate and efficient privacy-preserving string matching that provide privacy against various attacks. In the first approach we apply hashing-based encoding on sub-strings (q-grams) to compare sensitive strings, while in the second approach we generate one-bit array from the sub-strings of a string to identify the longest common bit sequences. We evaluate our approaches on several data sets with different types of strings, and validate their privacy, accuracy, and complexity compared to three baseline techniques, showing that they outperform all baselines.

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BackgroundAlthough captured and cultivated marine shrimp constitute highly important seafood in terms of both economic value and production quantity, biologists have little knowledge of the shrimp genome and this partly hinders their ability to improve shrimp aquaculture. To help improve this situation, the Shrimp Gene and Protein Annotation Tool (ShrimpGPAT) was conceived as a community-based annotation platform for the acquisition and updating of full-length complementary DNAs (cDNAs), Expressed Sequence Tags (ESTs), transcript contigs and protein sequences of penaeid shrimp and their decapod relatives and for in-silico functional annotation and sequence analysis.DescriptionShrimpGPAT currently holds quality-filtered, molecular sequences of 14 decapod species (~500,000 records for six penaeid shrimp and eight other decapods). The database predominantly comprises transcript sequences derived by both traditional EST Sanger sequencing and more recently by massive-parallel sequencing technologies. The analysis pipeline provides putative functions in terms of sequence homologs, gene ontologies and protein-protein interactions. Data retrieval can be conducted easily either by a keyword text search or by a sequence query via BLAST, and users can save records of interest for later investigation using tools such as multiple sequence alignment and BLAST searches against pre-defined databases. In addition, ShrimpGPAT provides space for community insights by allowing functional annotation with tags and comments on sequences. Community-contributed information will allow for continuous database enrichment, for improvement of functions and for other aspects of sequence analysis.ConclusionsShrimpGPAT is a new, free and easily accessed service for the shrimp research community that provides a comprehensive and up-to-date database of quality-filtered decapod gene and protein sequences together with putative functional prediction and sequence analysis tools. An important feature is its community-based functional annotation capability that allows the research community to contribute knowledge and insights about the properties of molecular sequences for better, shared, functional characterization of shrimp genes. Regularly updated and expanded with data on more decapods, ShrimpGPAT is publicly available at http://shrimpgpat.sc.mahidol.ac.th/.