抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Evolution of Protein Molecules

Antibiotic resistance is a silent pandemic, causing 700 thousand human deaths across the world every year. Antibiotic resistance genes (ARG) are genes conferring resistance for the bacteria carrying them. Predicting ARGs is an important computational task. Traditionally ARGs are predicted using alignment based methods. However, the false negative rate for most of the alignment-based tools is very high. The protein language models (LM) trained on the huge corpus protein sequences capture distant relations among protein sequences. These features can be utilized for the identification and classification of ARGs. We have presented a self-supervised model on the largest available ARG database with the help of a pre-trained language model ProtAlbert. We used the raw protein LM-embeddings from unlabeled data on our ARG classification task and saw it outperform state-of-the-art prediction algorithms. The extracted features from the pretrained language model boosted the supervised model accuracy to a great margin.

LM-ARG: Identification & classification of antibiotic resistance genes leveraging pre-trained protein language models

Protein complexes serve as functional modules in various biological processes. Computational predication of protein complexes is one of the most important challenges in the post-genomic era. In this paper, we take into account the biological function of proteins to reconstruct protein-protein interaction networks and propose a functional association-based method (FABM) to predict protein complexes in S. cerevisiae. The results show that FABM outperforms MCODE, MCL, and SPICi in identifying non-overlapping protein complexes. Reconstruction of the PPI network as a preprocessing step also helps to improve the performance of other graph clustering algorithms.

A new functional association-based protein complex prediction

Biological DNA reads are often trimmed before mapping, genome assembly, and other tasks to improve the quality of the results. Biological sequence complexity relates to alignment quality as low complexity regions can align poorly. There are many read trimmers, but many do not use sequence complexity for trimming. Alignment of reads generated from whole genome bisulfite sequencing is especially challenging since bisulfite treated reads tend to reduce sequence complexity. InfoTrim, a new read trimmer, was created to explore these issues. It is evaluated against five other trimmers using four read mappers on real and simulated bisulfite treated DNA data. InfoTrim has reasonable results that are consistent with other read mappers.

/pdf/infotrim-a-dna-read-quality-trimmer-using-entropy-1qpzp4e3db.pdf

InfoTrim: A DNA Read Quality Trimmer Using Entropy

Pluripotent stem cells can exist in either the naive state representing a developmental blank slate or the downstream primed state poised for differentiation. Currently, known differences between these two states are mostly phenomenological, and none can adequately explain why the two states should differ in developmental priming. Gene occlusion is a mode of epigenetic inactivation that renders genes unresponsive to their cognate transcriptional activators. It plays a crucial role in lineage restriction. Here, we report that a defining feature distinguishing the two pluripotent states lies in the ability of naive but not primed cells to erase occlusion. This "deocclusion" capacity requires Esrrb, a gene expressed only in the naive but not primed state. Notably, Esrrb silencing in the primed state is itself due to occlusion. Collectively, our data argue that the Esrrb-dependent deocclusion capacity in naive cells is key for sustaining naive pluripotency, and the loss of this capacity in the primed state via the occlusion of Esrrb poises cells for differentiation.

/pdf/capacity-to-erase-gene-occlusion-is-a-defining-feature-1hqkq9rti5.pdf

Capacity to erase gene occlusion is a defining feature distinguishing naive from primed pluripotency

This paper employs Fisher's model of adaptation to understand the expected fitness effect of fixing a mutation in a natural population. Fisher's model in one dimension admits a closed form solution for this expected fitness effect. A combination of different parameters, including the distribution of mutation lengths, population sizes, and the initial state that the population is in, are examined to see how they affect the expected fitness effect of state transitions. The results show that the expected fitness change due to the fixation of a mutation is always positive, regardless of the distributional shapes of mutation lengths, effective population sizes, and the initial state that the population is in. The further away the initial state of a population is from the optimal state, the slower the population returns to the optimal state. Effective population size (except when very small) has little effect on the expected fitness change due to mutation fixation. The always positive expected fitness change suggests that small populations may not necessarily be doomed due to the runaway process of fixation of deleterious mutations.

/pdf/the-expected-fitness-cost-of-a-mutation-fixation-under-the-3l80wlvua6.pdf

Liqing Zhang

Papers

LM-ARG: Identification & classification of antibiotic resistance genes leveraging pre-trained protein language models

A new functional association-based protein complex prediction

InfoTrim: A DNA Read Quality Trimmer Using Entropy

Capacity to erase gene occlusion is a defining feature distinguishing naive from primed pluripotency

The expected fitness cost of a mutation fixation under the one-dimensional fisher model