Research & Activities


We are passionate about developing new algorithms, machine learning and deep learning methods, and their applications to genomics, metagenomics and cancer research. Some of the current projects in the lab include characterizing human genomes and metagenomes sequenced by exciting new technologies, quantifying cancer evolution, study of tumor heterogeneity using genomics and digital pathology images.
Iman is teaching a Data Structures and Algorithms for Computational Biology class in Fall 2018. A tentative course schedule can be found here . Note tthat the schedule may slightly change to cover other topics, accommodate guest presenters and student needs.
View Research & Other Activities →

Structural Variation

Developing algorithms for characterizing structural variations focused on complex and repetitive elements, rare variants and clinical relevant variants.

Tumor Heterogeneity

Quantifying cancer evolution in multiple samples and reconstruction of tumor lineage trees using novel computational methods. Also, building deep learning based classifiers to discriminate heterogeneous sample, using imaging data.

Metagenomics

Advancing computational methods in metagenomics for the purpose of deconvolving mixtures, discovering variants and de novo assembly.


Most recent accepted manuscripts and preprints


6) Danko, Meleshko, Bezdan, Mason, Hajirasouliha. Minerva: An Alignment and Reference Free Approach to Deconvolve Linked-Reads for Mietagenomics (bioRxiv 217869)
5) Nima Habibzadeh Motlagh, Mahboobeh Jannesary, HamidReza Aboulkheyr, Pegah Khosravi , Olivier Elemento, Mehdi Totonchi, Iman Hajirasouliha Breast Cancer Histopathological Image Classification: A Deep Learning Approach (bioRxiv 242818)
4) Toosi, Moieni, Hajirasouliha. BAMSE: Bayesian model selection for tumor phylogeny inference among multiple samples (accepted in ICCABS 2017 and BMC)
3) Simone Ciccolella , Mauricio Soto Gomez, Murray Patterson, Gianluca Della Vedova, Iman Hajirasouliha, Paola Bonizzoni Inferring Cancer Progression from Single Cell Sequencing while allowing loss of mutations (bioRxiv 268243)
2) Malikic, Ciccolella, Ricketts, Hach, Haghshenas, Rahman, Rashidi Mehrabadi, Seidman, Hajirasouliha*, Sahinalp*. SCePhy - A Constraint Satisfaction Approach for Tumor Phylogeny Reconstruction via Integrative use f Single Cell and Bulk Sequencing Data
1) Meleshko, Mohimani, Hajirasouliha, Medema, Korobeynikov, Pevzner. Reconstructing Biosynthetic Gene Clusters From Assembly Graphs


Publications


Below you can see some highlights of our published work. Please also check publications page →

1000 Genomes Project

A catalog of human genome variations in population-scale.

Bioinformatics

Various methods in Bioinformatics and/or ISMB conferences. Algorithms for Next Generation Sequencing, Structural Variation discovery, tumor heterogeneity and Protein-protein intraction prediction.

Handling Multiple Sequenced Genomes

The CommonLAW package presented on the cover of Genome Research introduces novel combinatorial formulations and algorithms for structural variation discovery among a number of sequenced donor genomes, with the help of a complete reference genome. CommonLAW significantly reduces the false positive rate in detecting structural variation events when compared with conventional methods. (Cover illustration by Azalia Musa, modified by Andres Wanner and Iman Hajirasouliha.)

Want to discuss a project?

We are always keen to establish new collaborations with computational scientists and experimentalists.

Contact us