报告摘要：With the advancements of next-generation sequencing technology,
it is now possible to study samples directly obtained from the
environment. Particularly, 16S rRNA gene sequences have been
frequently used to profile the diversity of organisms underlying an
environmental sample. However, such studies are still taxed to
determine both the number of operational taxonomic units (OTUs) and
their relative abundance in a sample. To address these challenges, we
propose an unsupervised Bayesian clustering method termed Clustering
16S rRNA for OTU Prediction (CROP). CROP can find clusters based on
the natural organization of data without setting a hard cutoff
threshold (3%/5%) as required by hierarchical clustering methods. By
applying our method to several datasets, we demonstrate that CROP is
robust against sequencing errors and that it produces more accurate
results than conventional hierarchical clustering methods. The second
generation sequencing technology also enables the study of the whole
transcriptome by shotgun sequencing of RNA sequences. In practice, the
de novo transcriptome assembly is the only choice to the study of the
transcriptome of organisms that do not have reference genome
sequences, and it can also be applied to identify novel transcripts
and structural variations in the gene regions of model organisms. The
de novo transcriptome assembly differs from the de novo genome
assembly in that it assembles multiple alternatively spliced gene
transcripts simultaneously instead of individual chromosome sequences.
We propose a new de novo transcriptome assembly called WEAV which
first partitions RNA-seq reads into clusters, and then for each
cluster, applies a de Bruijn graph based method to simultaneously
identify multiple alternatively spliced gene transcripts.

报告人简介：Professor Ting Chen received his Ph.D. in Computer Science from
Computer Science Department, SUNY at Stony Brook in 1997 and is now an
associate professor of Computer Science in University of Southern
California,. Within the fields of computational biology and
bioinformatics, Professor Chen seeks to apply computer algorithms and
mathematical methods to answer questions in biology and medicine,
specifically in studies of human genetics, proteomics, and genomics.
Dr. Chen's research includes (1) large-scale sequence analysis for the
next generation sequencing data, (2) analysis of genetic variations of
the human genome for their relationships to human diseases, (3)
studies of functions and dynamic of large-scale biochemical networks
inside the cell, and (4) identification of proteins through analyzing
mass spectrometry data. Detailed information about Dr. Chen can be
found at http://www.cmb.usc.edu/people/tingchen/