Csaba Kerepesi

AmphoraNet: The webserver implementation of the AMPHORA2 metagenomic workflow suite

MOTIVATION Metagenomics went through an astonishing development in the past few years. Today not only gene sequencing experts, but numerous laboratories of other specializations need to analyze DNA sequences gained from clinical or environmental samples. Phylogenetic analysis of the metagenomic data presents significant challenges for the biologist and the bioinformatician. The program suite AMPHORA and its workflow version are examples of publicly available software that yields reliable phylogenetic results for metagenomic data. RESULTS Here we present AmphoraNet, an easy-to-use webserver that is capable of assigning a probability-weighted taxonomic group for each phylogenetic marker gene found in the input metagenomic sample; the webserver is based on the AMPHORA2 workflow. Since a large proportion of molecular biologists uses the BLAST program and its clones on public webservers instead of the locally installed versions, we believe that the occasional user may find it comfortable that, in this version, no time-consuming installation of every component of the AMPHORA2 suite or expertise in Linux environment is required. AVAILABILITY The webserver is freely available at http://amphoranet.pitgroup.org; no registration is required.

Parameterizable consensus connectomes from the Human Connectome Project: the Budapest Reference Connectome Server v3.0

Connections of the living human brain, on a macroscopic scale, can be mapped by a diffusion MR imaging based workflow. Since the same anatomic regions can be corresponded between distinct brains, one can compare the presence or the absence of the edges, connecting the very same two anatomic regions, among multiple cortices. Previously, we have constructed the consensus braingraphs on 1015 vertices first in five, then in 96 subjects in the Budapest Reference Connectome Server v1.0 and v2.0, respectively. Here we report the construction of the version 3.0 of the server, generating the common edges of the connectomes of variously parameterizable subsets of the 1015-vertex connectomes of 477 subjects of the Human Connectome Project’s 500-subject release. The consensus connectomes are downloadable in CSV and GraphML formats, and they are also visualized on the server’s page. The consensus connectomes of the server can be considered as the “average, healthy” human connectome since all of their connections are present in at least k subjects, where the default value of

Soda pans of the Pannonian steppe harbor unique bacterial communities adapted to multiple extreme conditions

k=209

Visual analysis of the quantitative composition of metagenomic communities: the AmphoraVizu webserver.

k=209, but it can also be modified freely at the web server. The webserver is available at http://connectome.pitgroup.org.

Giant viruses of the Kutch Desert

The human braingraph or the connectome is the object of an intensive research today. The advantage of the graph-approach to brain science is that the rich structures, algorithms and definitions of graph theory can be applied to the anatomical networks of the connections of the human brain. In these graphs, the vertices correspond to the small (1–1.5 cm2) areas of the gray matter, and two vertices are connected by an edge, if a diffusion-MRI based workflow finds fibers of axons, running between those small gray matter areas in the white matter of the brain. One main question of the field today is discovering the directions of the connections between the small gray matter areas. In a previous work we have reported the construction of the Budapest Reference Connectome Server http://connectome.pitgroup.org from the data recorded in the Human Connectome Project of the NIH. The server generates the consensus braingraph of 96 subjects in Version 2, and of 418 subjects in Version 3, according to selectable parameters. After the Budapest Reference Connectome Server had been published, we recognized a surprising and unforeseen property of the server. The server can generate the braingraph of connections that are present in at least k graphs out of the 418, for any value of k = 1, 2, …, 418. When the value of k is changed from k = 418 through 1 by moving a slider at the webserver from right to left, certainly more and more edges appear in the consensus graph. The astonishing observation is that the appearance of the new edges is not random: it is similar to a growing shrub. We refer to this phenomenon as the Consensus Connectome Dynamics. We hypothesize that this movement of the slider in the webserver may copy the development of the connections in the human brain in the following sense: the connections that are present in all subjects are the oldest ones, and those that are present only in a decreasing fraction of the subjects are gradually the newer connections in the individual brain development. An animation on the phenomenon is available at https://youtu.be/yxlyudPaVUE. Based on this observation and the related hypothesis, we can assign directions to some of the edges of the connectome as follows: Let Gk + 1 denote the consensus connectome where each edge is present in at least k+1 graphs, and let Gk denote the consensus connectome where each edge is present in at least k graphs. Suppose that vertex v is not connected to any other vertices in Gk+1, and becomes connected to a vertex u in Gk, where u was connected to other vertices already in Gk+1. Then we direct this (v, u) edge from v to u.

The braingraph.org database of high resolution structural connectomes and the brain graph tools

Soda pans of the Pannonian steppe are unique environments regarding their physical and chemical characteristics: shallowness, high turbidity, intermittent character, alkaline pH, polyhumic organic carbon concentration, hypertrophic condition, moderately high salinity, sodium and carbonate ion dominance. The pans are highly productive environments with picophytoplankton predominance. Little is known about the planktonic bacterial communities inhabiting these aquatic habitats; therefore, amplicon sequencing and shotgun metagenomics were applied to reveal their composition and functional properties. Results showed a taxonomically complex bacterial community which was distinct from other soda lakes regarding its composition, e.g. the dominance of class Alphaproteobacteria was observed within phylum Proteobacteria. The shotgun metagenomic analysis revealed several functional gene components related to the harsh and at the same time hypertrophic environmental conditions, e.g. proteins involved in stress response, transport and hydrolase systems targeting phytoplankton-derived organic matter. This is the first detailed report on the indigenous planktonic bacterial communities coping with the multiple extreme conditions present in the unique soda pans of the Pannonian steppe.

Life without dUTPase

Mimivirus was identified in 2003 from a biofilm of an industrial water-cooling tower in England. Later, numerous new giant viruses were found in oceans and freshwater habitats, some of them having 2,500 genes. We have demonstrated their likely presence in four soil samples taken from the Kutch Desert (Gujarat, India). Here we describe a bioinformatics work-flow, called the “Giant Virus Finder” that is capable of discovering the likely presence of the genomes of giant viruses in metagenomic shotgun-sequenced datasets. The new workflow is applied to numerous hot and cold desert soil samples as well as some tundra- and forest soils. We show that most of these samples contain giant viruses, especially in the Antarctic dry valleys. The results imply that giant viruses could be frequent not only in aqueous habitats, but in a wide spectrum of soils on our planet.

Evaluating the Quantitative Capabilities of Metagenomic Analysis Software

Low-cost DNA sequencing methods have given rise to an enormous development of metagenomics in the past few years. One basic— and difficult—task is the phylogenetic annotation of the metagenomic samples studied. The difficulty comes from the fact that the typical environmental sample contains hundreds of unknown and still uncharacterized microorganisms. There are several possible methods to assign at least partial phylogenetic information to these uncharacterized data. Originally, the 16S ribosomal RNA was used as phylogenetic marker, then genome sequence alignments and similarity measures between the unknown genome and the reference genomes were applied (e.g., in the MEGAN software), and more recently, phylogeny–based methods applying suitable sets of marker genes were suggested (AMPHORA, AMPHORA2, and the webserver implementation AmphoraNet). Here, we present a visual analysis tool that is capable of demonstrating the quantitative relations gained from the output of the AMPHORA2 program or the easy–to–use AmphoraNet webserver. Our web-based tool, the AmphoraVizu webserver, makes the phylogenetic distribution of the metagenomic sample clearly visible by using the native output format of AMPHORA2 or AmphoraNet. The user may set the phylogenetic resolution (i.e., superkingdom, phylum, class, order, family, genus, and species) along with the chart type and will receive the distribution data detailed for all relevant marker genes in the sample. For publication quality results, the chart labels can be customized by the user. The visualization webserver is available at the address http://amphoravizu.pitgroup.org. The AmphoraNet webserver is available at http://amphoranet.pitgroup.org. The open-source version of the AmphoraVizu program is available for download at http://pitgroup.org/apps/amphoravizu/AmphoraVizu.pl.