Novel Data Structures And Scalable Algorithms For High Throughput Bioinformatics

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Latest advances in sequencing technologies, especially those from Illumina, 10X Genomics, Pacific Biosciences, and Oxford Nanopore Technologies, are opening up new possibilities and new fields of research. These instruments demonstrate a sustained trend of expanding sequencing throughput, growing read lengths, and improving data quality. In parallel, the cost of using these platforms reached an inflection point, whereby they became increasingly viable for widespread applications across life sciences. However, this translation requires enabling bioinformatics approaches. We propose a bioinformatics project to develop novel data structures specialized for large sequencing datasets, and an innovative RNA-seq assembly tool to leverage the properties of the latest sequencing platforms. Accordingly, we have developed a research plan with two aims. Aim 1. Advanced Data Structures The value of innovative data types in bioinformatics applications has been demonstrated several times. The most prominent example of this is the use of FM-indexing for rapid read alignments. Here, we will build on our extensive expertise with Bloom filters and spaced seeds to address memory and run time bottlenecks in bioinformatics applications. Particularly, we will develop error tolerant methods for the sequence classification problem, where a set of high throughput sequencing reads are assigned to a set of reference genomes and/or genomic loci. Results of this aim will also support the research activities in the second aim of our proposal. Aim 2. RNA-seq Assembly RNA-seq experiments, often in combination with genome sequencing, have proven useful in studying the biology of model and non-model species. Transcriptome analysis based on de novo assembly has demonstrated utility for discovery in many projects, but its routine application in translational studies may be computationally costly, hence requires a rethinking of the problem. Using the advanced data types we will develop in Aim 1, we will leverage the new information modalities in recent sequencing technologies, such as single cell RNA sequencing (scRNA-seq). Our lab has an established track record of developing, disseminating, and maintaining popular bioinformatics tools built on advanced computational methods. We will implement and release our tools and algorithms through our lab's software portal at https://github.com/bcgsc, providing the research community broad and timely access to these enabling technologies, and offering active support. We will also continue to collaborate widely across life sciences domains to apply our analytical methods, and support basic and applied research projects. The aims of this research plan are in response to the identified needs of our collaborators and end users. Last but not the least, we expect this project to serve as a platform to train a number graduate students and interns/co-op students.