MOST+: A de novo motif finding approach combining genomic sequence and heterogeneous genome-wide signatures

Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning

Motivation

The study of transcriptional regulation is still difficult yet fundamental in molecular biology research. While the development of both in vivo and in vitro profiling techniques have significantly enhanced our knowledge of transcription factor (TF)-DNA interactions, computational models of TF-DNA interactions are relatively simple and may not reveal sufficient biological insight. In particular, supervised learning based models for TF-DNA interactions attempt to map sequence-level features ( k -mers) to binding event but usually ignore the location of k -mers, which can cause data fragmentation and consequently inferior model performance.

Results

Here, we propose a novel algorithm based on the so-called multiple-instance learning (MIL) paradigm. MIL breaks each DNA sequence into multiple overlapping subsequences and models each subsequence separately, therefore implicitly takes into consideration binding site locations, resulting in both higher accuracy and better interpretability of the models. The result from both in vivo and in vitro TF-DNA interaction data show that our approach significantly outperform conventional single-instance learning based algorithms. Importantly, the models learned from in vitro data using our approach can predict in vivo binding with very good accuracy. In addition, the location information obtained by our method provides additional insight for motif finding results from ChIP-Seq data. Finally, our approach can be easily combined with other state-of-the-art TF-DNA interaction modeling methods.

Availability and Implementation

http://www.cs.utsa.edu/∼jruan/MIL/.

Contact

jianhua.ruan@utsa.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Computational insights into promoter architecture of toxin-antitoxin systems of Mycobacterium tuberculosis

Toxin-antitoxin (TA) systems are two component genetic modules widespread in many bacterial genomes, including Mycobacterium tuberculosis (Mtb). The TA systems play a significant role in biofilm formation, antibiotic tolerance and persistence of pathogen inside the host cells. Deciphering regulatory motifs of Mtb TA systems is the first essential step to understand their transcriptional regulation. In this study, in silico approaches, that is, the knowledge based motif discovery and de novo motif discovery were used to identify the regulatory motifs of 79 Mtb TA systems. The knowledge based motif discovery approach was used to design a Perl based bio-tool Mtb-sig-miner available at (https://github.com/zoozeal/Mtb-sig-miner), which could successfully detect sigma (σ) factor specific regulatory motifs in the promoter region of Mtb TA modules. The manual curation of Mtb-sig-miner output hits revealed that the majority of them possessed σ^B regulatory motif in their promoter region. On the other hand, de novo approach resulted in the identification of a novel conserved motif [(T/A)(G/T)NTA(G/C)(C/A)AT(C/A)] within the promoter region of 14 Mtb TA systems. The identified conserved motif was also validated for its activity as conserved core region of operator sequence of corresponding TA system by molecular docking studies. The strong binding of respective antitoxin/toxin with the identified novel conserved motif reflected the validation of identified motif as the core region of operator sequence of respective TA systems. These findings provide computational insight to understand the transcriptional regulation of Mtb TA systems.

Transcription factor motif quality assessment requires systematic comparative analysis

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. We also demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.

Transcription factor motif quality assessment requires systematic comparative analysis

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. Finally, we demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.

Education, collaboration, and innovation: intelligent biology and medicine in the era of big data

Here we present a summary of the 2014 International Conference on Intelligent Biology and Medicine (ICIBM 2014) and the editorial report of the supplement to BMC Genomics and BMC Systems Biology that includes 20 research articles selected from ICIBM 2014. The conference was held on December 4-6, 2014 at San Antonio, Texas, USA, and included six scientific sessions, four tutorials, four keynote presentations, nine highlight talks, and a poster session that covered cutting-edge research in bioinformatics, systems biology, and computational medicine.