The European Conference on Computational Biology was held in Strasbourg, France this year. The conference was attended by several members of OPIG including Charlotte Deane, Waqar Ali, Eleanor Law, Jaroslaw Nowak and Cristian Regep. Waqar gave a talk on his paper proposing a new distance measure for network comparison (Netdis). There were many interesting talks and posters at the conference, and brief summaries of the ones we found most relevant are given below.

The impact of incomplete knowledge on the evaluation of protein function prediction: a structured output learning perspective.

Authors: Yuxiang Jiang, Wyatt T. Clark, Iddo Friedberg and Predrag Radivojac

Chosen by: Waqar Ali

The automated functional annotation of biological macromolecules is a problem of computational assignment of biological concepts or ontological terms to genes and gene products. A number of methods have been developed to computationally annotate genes using standardized nomenclature such as Gene Ontology (GO). One important concern is that experimental annotations of proteins are incomplete. This raises questions as to whether and to what degree currently available data can be reliably used to train computational models and estimate their performance accuracy.

In the paper, the authors studied the effect of incomplete experimental annotations on the reliability of performance evaluation in protein function prediction. Using the structured-output learning framework, they provide theoretical analyses and carry out simulations to characterize the effect of growing experimental annotations on the correctness and stability of performance estimates corresponding to different types of methods. They also analyzed real biological data by simulating the prediction, evaluation and subsequent re-evaluation (after additional experimental annotations become available) of GO term predictions. They find a complex interplay between the prediction algorithm, performance metric and underlying ontology. However, using the available experimental data and under realistic assumptions, their results also suggest that current large-scale evaluations are meaningful and surprisingly reliable. The choice of function prediction methods evaluated by the authors is not exhaustive however and it is quite possible that other methods might be much more sensitive to incomplete annotations.

Towards practical, high-capacity, low-maintenance information storage in synthesized DNA.

Authors: Nick Goldman, Paul Bertone, Siyuan Chen, Christophe Dessimoz, Emily M. LeProust, Botond Sipos & Ewan Birney

Chosen by: Jaroslaw Nowak

This was one of the keynote speaker talks. One of the authors, Ewan Birney discussed how viable is storing digital information in DNA code. The paper talked about storing 739 kilobytes of hard-disk storage in the genetic code. The data stored included all 154 of Shakespeare’s sonnets in ASCII text, a scientific paper in PDF format, a medium-resolution colour photograph of the European Bioinformatics Institute in JPEG format, a 26-s excerpt from Martin Luther King’s 1963 ‘I have a dream’ speech in MP3 format and a Huffman code used in their study to convert bytes to base-3 digits (ASCII text). The authors accomplished this by first converting the binary data into base-3 numbers using Huffman coding (which represents the most common piece of information using the least bits). The base-3 numbers where then converted into a genetic code in such a way that produced no homopolymers. The authors also proved that they can read the encoded information and reconstruct the data with 100% accuracy.

Using DNA for information storage could very soon become cost-effective for sub-50 years archiving. The current costs of the process are $12,400/MB for writing and $220/MB for reading information, with negligible costs of copying. Nevertheless, if the current trends persist, we could see a 100 – fold drop in costs in less than a decade. The main advantages of storing information in DNA is the low maintenance and durability of the medium (intact DNA fragments have been recovered from samples that are tens of thousands years old) as well as little physical space required to store the information (~2.2 PB/g)

PconsFold: Improved contact predictions improve protein models.

Authors: Michel M, Hayat S, Skwark MJ, Sander C, Marks DS and Elofsson A.

Chosen by: Eleanor Law

De novo structure prediction by fragment assembly is a very difficult task, but can be aided by contact prediction in cases where there is plenty of sequence data. Contact prediction has also significantly improved recently, using statistical methods to separate direct from indirect contact information.

PSICOV and plmDCA are two such methods, providing contacts which can be used by software such as Rosetta as an additional energy term. PconsFold combines 16 different sets of contact predictions by these programs, built from different sequence alignments, with secondary structure and solvent accessibility prediction. The output of the deep learning process on these inputs is more reliable that the individual contact predictions alone, and produces more accurate models. The authors found that using only 2,000 decoys rather than 20,000 did not greatly harm their results, which is encouraging as the decoy generation stage is the particularly resource intensive stage. Using a balance between the Rosetta energy function and weight of contact prediction, the optimal number of constraints to include was around 2 per residue, compared to 0.5 per residue for PSICOV or plmDCA alone.

The PconsFold pipeline is not always able to make full use of the contact prediction, as accuracy of contact prediction on the true structure can be higher than that in the model. This is a case where the conformational search is not effective enough to reach the correct answer, though it would be scored correctly if it were obtained. All-beta proteins are the most difficult to predict, but PconsFold compares favourably to EVfold-PLM for each of the mainly alpha, mainly beta, and alpha & beta classes.