The Douglas E. Eveleigh Travel Award for graduate students is awarded each year by the Department of Biochemistry and Microbiology of the Rutgers School of Environmental and Biological Sciences. This year Yannick Mahlich is one of the recipients, to support his international travel to scientific conferences.
A paper came out in Human mutation, variation, informatics and desease, with collaborators Marco Carraro, Giovanni Minervini, Manuel Giollo, Emidio Capriotti, Rita Casadio, Roland Dunbrack, Lisa Elefanti, Pietro Fariselli, Carlo Ferrari, Julian Gough, Panagiotis Katsonis, Emanuela Leonardi, Olivier Lichtarge, Chiara Menin, Pier Luigi Martelli, Abhishek Niroula, Lipika R. Pal, Susanna Repo, Maria Chiara Scaini, Mauno Vihinen, Qiong Wei, Qifang Xu, Yuedong Yang, Yizhou Yin, Jan Zaucha, Huiying Zhao, Yaoqi Zhou, Steven E.
We attended the ISMB 2018 conference of the ISCB (International Society of Computational Biology) from July 6 to July 10 at Chicago.
Yannick gave a talk titled HFSP: High speed homology-driven function annotation of proteins.
Chengsheng gave a talk titled mi-faser deciphers city subway microbiome functional fingerprints.
Max gave a talk titled Identifying protein positions for variation driven functional tuning.
Yanran gave a talk titled Identifying Crohn’s disease signal from variome analysis.
Congratulations to Chengsheng, who won the Best Talk prize on July 8 for the CAMDA (Critical Assessment of Massive Data Analysis) challenge during the ISMB 2018 conference of the ISCB (International Society of Computational Biology).
HFSP: high speed homology-driven function annotation of proteins Yannick Mahlich, Martin Steinegger, Burkhard Rost, Yana Bromberg
Bioinformatics, Volume 34, Issue 13, 1 July 2018, Pages i304–i312 https://doi.org/10.1093/bioinformatics/bty262
The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins.