Moscow Institute of Physics and Technology

About: Moscow Institute of Physics and Technology is a education organization based out in Dolgoprudnyy, Russia. It is known for research contribution in the topics: Laser & Large Hadron Collider. The organization has 8594 authors who have published 16968 publications receiving 246551 citations. The organization is also known as: MIPT & Moscow Institute of Physics and Technology (State University).

Assessment of k -mer spectrum applicability for metagenomic dissimilarity analysis

Abstract: A rapidly increasing flow of genomic data requires the development of efficient methods for obtaining its compact representation. Feature extraction facilitates classification, clustering and model analysis for testing and refining biological hypotheses. “Shotgun” metagenome is an analytically challenging type of genomic data - containing sequences of all genes from the totality of a complex microbial community. Recently, researchers started to analyze metagenomes using reference-free methods based on the analysis of oligonucleotides (k-mers) frequency spectrum previously applied to isolated genomes. However, little is known about their correlation with the existing approaches for metagenomic feature extraction, as well as the limits of applicability. Here we evaluated a metagenomic pairwise dissimilarity measure based on short k-mer spectrum using the example of human gut microbiota, a biomedically significant object of study. We developed a method for calculating pairwise dissimilarity (beta-diversity) of “shotgun” metagenomes based on short k-mer spectra (5≤k≤11). The method was validated on simulated metagenomes and further applied to a large collection of human gut metagenomes from the populations of the world (n=281). The k-mer spectrum-based measure was found to behave similarly to one based on mapping to a reference gene catalog, but different from one using a genome catalog. This difference turned out to be associated with a significant presence of viral reads in a number of metagenomes. Simulations showed limited impact of bacterial genetic variability as well as sequencing errors on k-mer spectra. Specific differences between the datasets from individual populations were identified. Our approach allows rapid estimation of pairwise dissimilarity between metagenomes. Though we applied this technique to gut microbiota, it should be useful for arbitrary metagenomes, even metagenomes with novel microbiota. Dissimilarity measure based on k-mer spectrum provides a wider perspective in comparison with the ones based on the alignment against reference sequence sets. It helps not to miss possible outstanding features of metagenomic composition, particularly related to the presence of an unknown bacteria, virus or eukaryote, as well as to technical artifacts (sample contamination, reads of non-biological origin, etc.) at the early stages of bioinformatic analysis. Our method is complementary to reference-based approaches and can be easily integrated into metagenomic analysis pipelines.

Unbiased False Discovery Rate Estimation for Shotgun Proteomics Based on the Target-Decoy Approach

Abstract: Target-decoy approach (TDA) is the dominant strategy for false discovery rate (FDR) estimation in mass-spectrometry-based proteomics. One of its main applications is direct FDR estimation based on counting of decoy matches above a certain score threshold. The corresponding equations are widely employed for filtering of peptide or protein identifications. In this work we consider a probability model describing the filtering process and find that, when decoy counting is used for q value estimation and subsequent filtering, a correction has to be introduced into these common equations for TDA-based FDR estimation. We also discuss the scale of variance of false discovery proportion (FDP) and propose using confidence intervals for more conservative FDP estimation in shotgun proteomics. The necessity of both the correction and the use of confidence intervals is especially pronounced when filtering small sets (such as in proteogenomics experiments) and when using very low FDR thresholds.

Performance of Machine Learning Algorithms in Predicting Game Outcome from Drafts in Dota 2

Abstract: In this paper we suggest the first systematic review and compare performance of most frequently used machine learning algorithms for prediction of the match winner from the teams’ drafts in Dota 2 computer game. Although previous research attempted this task with simple models, weve made several improvements in our approach aiming to take into account interactions among heroes in the draft. For that purpose we’ve tested the following machine learning algorithms: Naive Bayes classifier, Logistic Regression and Gradient Boosted Decision Trees. We also introduced Factorization Machines for that task and got our best results from them. Besides that, we found that model’s prediction accuracy depends on skill level of the players. We’ve prepared publicly available dataset which takes into account shortcomings of data used in previous research and can be used further for algorithms development, testing and benchmarking.

Dissection of affinity captured LINE-1 macromolecular complexes

Abstract: Our genome consists of about two percent genes, while around 60 to 70 percent are made up of hundreds of thousands of copies of very similar DNA sequences. These repeats have accumulated over time due to specific genetic elements called transposons. Transposons are often referred to as ‘jumping genes’, as they can move within the genome and thereby create mutations that may lead to cancer or other genetic diseases. LINE-1 is the only remaining active transposon in humans, and it expands by copying and pasting itself to new locations. To do so, it is first transcribed into RNA – the molecules that help to make proteins – and then converted back into identical DNA sequences. In a never-ending battle, our cells have been fighting to keep LINE-1 and its ancestors from replicating, and so evolved various defense mechanisms. Yet, LINE-1 has learned to circumvent these barriers, and continues to replicate and cause disease. Our understanding of these defenses and of how LINE-1 evades them is limited. Previous research has shown that the LINE-1 RNA and its two encoded proteins, called ORF1p and ORF2p, interact with a series of other proteins, with which they can form different types of complexes. Now, Taylor, Altukhov, Molloy et al. used human embryonic kidney cells grown in the laboratory with different LINE-1 mutations to identify how they affect the bound proteins and RNAs. The results showed that LINE-1 can form at least two different sets of complexes with other proteins. The complex containing ORF1p and ORF2p and several other proteins was located in the cytoplasm, the fluid that fills the cells. However, the experiments also revealed a new complex in the cell nucleus, which contained ORF2p and proteins involved in DNA replication and repair, but not ORF1p. The results suggest ORF1p delivers RNPs to the nucleus around the time the cell divides. Another group of researchers has looked more closely at what happens during cell division. A next step will be to study how exactly LINE-1 contributes to cancer. In the future, overactive LINE-1 proteins could be targeted to kill cancer cells, to identify cancer early, or to see if the cancer has come back. LINE-1 may also provide clues on how the genome has evolved.