Inference of single-cell phylogenies from lineage tracing data using Cassiopeia.

The pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia-a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together, these should broadly enable large-scale mammalian lineage tracing efforts. Cassiopeia and its benchmarking resources are publicly available at www.github.com/YosefLab/Cassiopeia

Pan-genome Analysis, Visualization and Exploration

The dynamics of prokaryotic genomes are driven by the intricate interplay of different evolutionary forces such as gene duplication, gene loss and horizontal transfer. Even closely related strains can exhibit remarkable genetic diversity and substantial gene presence/absence variation. The pan-genome, namely the complete inventory of genes in a collection of strains, can be several times larger than the genome of any single strain. Although several tools for pan-genome analysis have been published, there is still much room for algorithmic improvement, as well as needs for applications that better interactively visualize and explore pan-genomes. Therefore, we have developed panX, an automated computational pipeline for efficient identification of orthologous gene clusters in the pan-genome. PanX identifies homologous relationships among genes using DIAMOND and MCL and then harnesses phylogeny-based post- processing to separate orthologs from paralogs. Furthermore, we take advantage of a divide-and-conquer strategy to achieve an approximately linear runtime on large datasets. The analysis result can be visualized by the accompanying software, an easy-to-use and powerful web-based visualization application for interactive exploration of the pan-genome. The visualization dashboard encompasses a variety of connected components that allow rapid searching, filtering and sorting of genes and flexible investigation of evolutionary relationships among strains and their genes. PanX seamlessly interlinks gene clusters with their alignments and gene phylogenies, maps mutations on the branches of gene tree and highlights gene gain and loss events on the core-genome phylogeny that can also be colored by metadata associated with strains. By using 120 simulated pan-genome datasets for benchmarking and comparing clustering results on real dataset between different tools, panX exhibits overall good performance across a large range of diversities. PanX is available at pangenome.de, with a wide range of microbial pan-genomes established. Besides, user-provided pan-genomes can be visualized either via a web server or by running panX locally as a web-based application

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure

AIP1 is a novel Agenet/Tudor domain protein from Arabidopsis that interacts with regulators of DNA replication, transcription and chromatin remodeling

Background: DNA replication and transcription are dynamic processes regulating plant development that are dependent on the chromatin accessibility. Proteins belonging to the Agenet/Tudor domain family are known as histone modification "readers" and classified as chromatin remodeling proteins. Histone modifications and chromatin remodeling have profound effects on gene expression as well as on DNA replication, but how these processes are integrated has not been completely elucidated. It is clear that members of the Agenet/Tudor family are important regulators of development playing roles not well known in plants. Methods: Bioinformatics and phylogenetic analyses of the Agenet/Tudor Family domain in the plant kingdom were carried out with sequences from available complete genomes databases. 3D structure predictions of Agenet/Tudor domains were calculated by I-TASSER server. Protein interactions were tested in two-hybrid, GST pulldown, semi-in vivo pulldown and Tandem Affinity Purification assays. Gene function was studied in a T-DNA insertion GABI-line. Results: In the present work we analyzed the family of Agenet/Tudor domain proteins in the plant kingdom and we mapped the organization of this family throughout plant evolution. Furthermore, we characterized a member from Arabidopsis thaliana named AIP1 that harbors Agenet/Tudor and DUF724 domains. AIP1 interacts with ABAP1, a plant regulator of DNA replication licensing and gene transcription, with a plant histone modification "reader" (LHP1) and with non modified histones. AIP1 is expressed in reproductive tissues and its down-regulation delays flower development timing. Also, expression of ABAP1 and LHP1 target genes were repressed in flower buds of plants with reduced levels of AIP1. Conclusions: AIP1 is a novel Agenet/Tudor domain protein in plants that could act as a link between DNA replication, transcription and chromatin remodeling during flower development