ISMB - Accepted Talks

ISMB - Accepted Talks

ISMB talks are announced. h/t: @nextgenseek

Following talks may be of interest to our readers.

Haplotype assembly in polyploid genomes and identical by descent shared tracts

Author: Derek Aguiar , Brown University, United States

Additional authors:

Sorin Istrail, Brown University,

We already covered this excellent work in our blog. So, we are not including the abstract here.


IDBA-Tran: A More Robust de novo de Bruijn Graph Assembler for Transcriptomes with Uneven Expression Levels

Author: Yu Peng , The University of Hong Kong, Hong Kong

Additional authors:

Henry C.M. Leung, The University of Hong Kong,

S.M. Yiu, The University of Hong Kong,

Xin-Guang Zhu, Shanghai Institutes for Biological Sciences,

Ming-Zhu Lv, Shanghai Institutes for Biological Sciences,

Francis Chin, The University of Hong Kong,

This is the RNAseq version of IDBA work by Yu Peng and co-authors. IDBA makes elegant use of multiple k-mers to improve a de Bruijn graph-based genome assembly. Please take a look at our earlier commentary - From Multiple Kmers to Multi-kmer de Bruijn Graph.



Using State Machines to Model the IonTorrent Sequencing Process and Improve Read Error-Rates

Author: David Golan , Tel Aviv University, Israel

Additional authors:

Paul Medvedev, The Pennsylvania State University.

Presentation Overview:

Motivation: The importance of fast and affordable DNA sequencing methods for current day life sciences, medicine and biotechnology is hard to overstate. A major player is IonTorrent, a pyrosequencing-like technology which produces flowgrams sequences of incorporation values which are converted into nucleotide sequences by a base-calling algorithm. Because of its exploitation of ubiquitous semiconductor technology and innovation in chemistry, IonTorrent has been gaining popularity since its debut in 2011. Despite the advantages, however, IonTorrent read accuracy remains a significant concern. Results: We present FlowgramFixer, a new algorithm for converting flowgrams into reads. Our key observation is that the incorporation signals of neighboring flows, even after normalization and phase correction, carry considerable mutual information and are important in making the correct base-call. We therefore propose that base-calling of flowgrams should be done on a read-wide level, rather than one flow at a time. We show that this can be done in linear time by combining a state machine with a Viterbi algorithm to find the nucleotide sequence that maximizes the likelihood of the observed flowgram. FlowgramFixer is applicable to any flowgram based sequencing platform. We demonstrate FlowgramFixers superior performance on Ion Torrent E.Coli data, with a 4.8% improvement in the number of high-quality mapped reads and a 7.1% improvement in the number of uniquely mappable reads. Availability: Binaries and source code of FlowgramFixer are freely available at:


Compressive genomics for protein databases

Author: Noah Daniels , Tufts University, United States

Presentation Overview:

Motivation: The exponential growth of protein sequence databases has increasingly made the fundamental question of searching for homologs a computational bottleneck. The amount of unique data, however, is not growing nearly as fast; we can exploit this fact to greatly accelerate homology search. Acceleration of programs in the popular PSI/DELTA-BLAST family of tools will not only speed up homology search directly, but also the huge collection of other current programs that primarily interact with large protein databases via precisely these tools. Results: We introduce a suite of homology search tools, powered by compressively-accelerated protein BLAST (CaBLASTP), which are significantly faster than and comparably accurate to all known state- of-the-art tools including HHblits, DELTA-BLAST, and PSI-BLAST. Further, our tools are implemented in a manner that allows direct substitution into existing analysis pipelines. The key idea is that we introduce a local similarity-based compression scheme that allows us to operate directly on the compressed data. Importantly, CaBLASTPs runtime scales almost linearly in the amount of unique data, as opposed to current BLASTP variants which scale linearly in the size of the full protein database being searched. Our compressive algorithms will speed up many tasks such as protein structure prediction and orthology mapping which rely heavily on homology search. Availability: CaBLASTP is available under the GNU Public License at


Short Read Alignment with Populations of Genomes

Author: Lin Huang , Stanford University, United States

Presentation Overview:

The increasing availability of high throughput sequencing technologies has led to thousands of human genomes having been sequenced in the past years. Efforts such as the 1000 Genomes Project further add to the availability of human genome variation data. However, to-date there is no method that can map reads of a newly sequenced human genome to a large collection of genomes. Instead, methods rely on aligning reads to a single reference genome. This leads to inherent biases and lower accuracy. To tackle this problem, a new alignment tool BWBBLE is introduced in this paper. We (1) introduce a new compressed representation of a collection of genomes, which explicitly tackles the genomic variation observed at every position, and (2) design a new alignment algorithm based on the Burrows-Wheeler transform that maps short reads from a newly sequenced genome to an arbitrary collection of 2 or more (up to millions of) genomes with high accuracy and no inherent bias to one specific genome.

The above talk comes from Serafim Batzoglou’s group. Serafim worked on ARACHNE assembler a decade back with Lander, and then joined Stanford. His LAGAN aligner was used in the first ENCODE project.


Design of Shortest Double-Stranded DNA Sequences Covering All K-mers with Applications to Protein Binding Microarrays and Synthetic Enhancers

Author: Yaron Orenstein , Tel-Aviv University, Israel

Presentation Overview:

Novel technologies can generate large sets of short double-stranded DNA sequences that can be used to measure their regulatory effects. Microarrays can measure in vitro the binding intensity of a protein to thousands of probes. Synthetic enhancer sequences inserted into an organism’s genome allow us to measure in vivo the effect of such sequences on the phenotype. In both applications, by using sequence probes that cover all k-mers, a comprehensive picture of the effect of all possible short sequences on gene regulation is obtained. The value of k that can be used in practice is, however, severely limited by cost and space considerations. A key challenge is therefore to cover all k-mers with a minimal number of probes.The standard way to do this uses the de Bruijn sequence of length 4^k. However, since probes are double stranded, when a k-mer is included in a probe, its reverse complement k-mer is accounted for as well. Here we show how to efficiently create a shortest possible sequence with the property that it contains each k-mer or its reverse complement, but not necessarily both. The length of the resulting sequence approaches half that of the de Bruijn sequence as k increases. By reducing the total sequence length, experimental limitations can be overcome; alternatively, additional sequences with redundant k-mers of interest can be added.


Poly(A) motif prediction using spectral latent features from human DNA sequences

Author: Bo Xie , Georgia Institute of Technology, United States

Additional authors:

Boris Yankovic, King Abdullah University of Science and Technology,

Vladimir Bajic, King Abdullah University of Science and Technology,

Le Song, Georgia Institute of Technology,

Xin Gao, King Abdullah University of Science and Technology,

Presentation Overview:

Motivation: Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA. Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in- depth domain knowledge. Results: We propose a novel machine learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we employed hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine tune the classification performance. We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14,740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of previous state-of-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false negative rate and false positive rate by 26%, 15% and 35%, respectively. Meanwhile, our method made about 30% fewer error predictions relative to the other string kernels. Furthermore, our method can be used to visualize the importance of oligomers and positions in predicting poly(A) motifs, from which we can observe a number of characteristics in the surrounding regions of true and false motifs that have not been reported before. Availability: website:

Written by M. //