http://compbio.cs.princeton.edu/function

BackgroundGenlisea aurea (Lentibulariaceae) is a carnivorous plant with unusually small genome size - 63.6 Mb – one of the smallest known among higher plants. Data on the genome sizes and the phylogeny of Genlisea suggest that this is a derived state within the genus. Thus, G. aurea is an excellent model organism for studying evolutionary mechanisms of genome contraction.ResultsHere we report sequencing and de novo draft assembly of G. aurea genome. The assembly consists of 10,687 contigs of the total length of 43.4 Mb and includes 17,755 complete and partial protein-coding genes. Its comparison with the genome of Mimulus guttatus, another representative of higher core Lamiales clade, reveals striking differences in gene content and length of non-coding regions.ConclusionsGenome contraction was a complex process, which involved gene loss and reduction of lengths of introns and intergenic regions, but not intron loss. The gene loss is more frequent for the genes that belong to multigenic families indicating that genetic redundancy is an important prerequisite for genome size reduction.

Evolution of SARS-CoV-2 in immunocompromised hosts may result in novel variants with changed properties. While escape from humoral immunity certainly contributes to intra-host evolution, escape from cellular immunity is poorly understood. Here, we report a case of long-term COVID-19 in an immunocompromised patient with non-Hodgkin’s lymphoma who received treatment with rituximab and lacked neutralizing antibodies. Over the 318 days of the disease, the SARS-CoV-2 genome gained a total of 40 changes, 34 of which were present by the end of the study period. Among the acquired mutations, 12 reduced or prevented the binding of known immunogenic SARS-CoV-2 HLA class I antigens. By experimentally assessing the effect of a subset of the escape mutations, we show that they resulted in a loss of as much as ~1% of effector CD8 T cell response. Our results indicate that CD8 T cell escape represents a major underappreciated contributor to SARS-CoV-2 evolution in humans.

NetGrep (http://genomics.princeton.edu/singhlab/netgrep/) is a system for searching protein interaction networks for matches to user-supplied 'network schemas'. Each schema consists of descriptions of proteins (for example, their molecular functions or putative domains) along with the desired topology and types of interactions among them. Schemas can thus describe domaindomain interactions, signaling and regulatory pathways, or more complex network patterns. NetGrep provides an advanced graphical interface for specifying schemas and fast algorithms for extracting their matches. RationaleHigh-throughput experimental and computational approaches to characterize proteins and their interactions have resulted in large-scale biological networks for many organisms. These complex networks are composed of a number of distinct types of interactions: these include interactions between proteins that interact physically, that participate in a synthetic lethal or epistatic relationship, that are coexpressed, or where one phosphorylates or regulates another (for a review, see [1]). Though incomplete and noisy, these networks provide a holistic view of the functioning of the cell, and with appropriate computational analysis and experimental work have significant potential for helping to uncover precisely how complex biological processes are accomplished.We have developed a network analysis system based on querying interactomes using templates corresponding to network patterns of interest. Searching for recurring patterns in biological data has been the backbone of much research in computational biology; for example, within the context of sequence analysis, it has given rise to extensive work on sequence alignments and sequence motif discovery and has resulted in large sequence motif libraries. Not surprisingly, within the burgeoning field of biological network analysis, considerable effort has been focused on uncovering recurring patterns within interactomes. Mapping homologous proteins with conserved interaction patterns in different interactomes has revealed shared modules and complexes recurring across a range of organisms [2][3][4][5][6]. Analysis of the wiring diagrams of interactomes has uncovered network motifs that occur more frequently than expected by chance [7][8][9][10][11][12][13]. Additionally, there has been much work on uncovering recurring domaindomain interactions in physical interactomes [14][15][16][17][18][19][20][21][22][23], both to suggest a physical basis for known interactions and to help predict new interactions. Most closely related to the work described here are previous attempts to query biological networks using particular user-supplied subgraphs [24][25][26][27][28][29].In this paper, we introduce a system, NetGrep, that integrates the wealth of prior information known about individual proteins -for example, their functional annotations, sequence

Delta has outcompeted most preexisting variants of SARS-CoV-2, becoming the globally predominant lineage by mid-2021. Its subsequent evolution has led to emergence of multiple sublineages, most of which are well-mixed between countries. By contrast, here we show that nearly the entire Delta epidemic in Russia has probably descended from a single import event, or from multiple closely timed imports from a single poorly sampled geographic location. Indeed, over 90% of Delta samples in Russia are characterized by the nsp2:K81N+ORF7a:P45L pair of mutations which is rare outside Russia, putting them in the AY.122 sublineage. The AY.122 lineage was frequent in Russia among Delta samples from the start, and has not increased in frequency in other countries where it has been observed, suggesting that its high prevalence in Russia has probably resulted from a random founder effect rather than a transmission advantage. The apartness of the genetic composition of the Delta epidemic in Russia makes Russia somewhat unusual, although not exceptional, among other countries.