Giant Viruses of the Kutch Desert

The Kutch desert (Great Rann of Kutch, Gujarat, India) is a unique ecosystem: in the larger part of the year it is a hot, salty desert that is flooded regularly in the Indian monsoon season. In the dry season, the crystallized salt deposits form the "white desert" in large regions. The first metagenomic analysis of the soil samples of Kutch was published in 2013, and the data was deposited in the NCBI Sequence Read Archive. The sequences were analyzed at the same time phylogenetically for prokaryotes, especially for bacterial taxa. In the present work, we are searching for the DNA sequences of the recently discovered giant viruses in the soil samples of the Kutch desert. Since most giant viruses were discovered in biofilms in industrial cooling towers, ocean water and freshwater ponds, we were surprised to find their DNA sequences in the soil samples of a seasonally very hot and arid, salty environment

Data Mining in Genomics, Metagenomics and Connectomics

Létrehoztuk az AmphoraNet-et, egy könnyen használható webszervert, amely minden egyes a metagenomban talált filogenetikai marker gén szekvenciához kijelöl egy rendszertani csoportot. A webszerver az AMPHORA2 munkafolyamaton alapul. Az AmphoraNet után kifejlesztettük az AmphoraVizu webszervert, amely az AmphoraNet nehezen feldolgozható szöveges outputjához nyújt interaktív képi megjelenítést. Ezek után kiértékeltük az általunk fejlesztett AmphoraNet+AmphoraVizu-t és két másik metagenomikai elemző szoftvert abból a szempontból, hogy mennyire írják le jól adott baktériumok előfordulási gyakoriságát ugyanazon mintában. Ezután kifejlesztettük a Giant Virus Finder szoftvert, amely képes kimutatni óriás vírus specifikus szekvenciák jelenlétét metagenomokban. Az új szoftver segítségével óriás vírusok jelenlétét mutattuk ki számos forró és hideg sivatagi talajmintában. Megvizsgáltuk az összes baktérium (2261 db) és archaea (151 db) teljes genomi szekvenciára, hogy tartalmaznak-e dUTPáz gént. Meglepő módon azt találtuk, hogy nagy számú baktérium és archaea fajban hiányzik a dUTPáz gén. Kifejlesztettük a Budapest Reference Connectome szervert, amely MRI felvételekből számolt agygráfokhoz számolja ki a referencia agygráfot. A szervert vizsgálva felfedeztünk egy meglepő tulajdonságot. Amikor a szerveren a maximum értéktől indulva csökkentjük a ”Minimum edge confidence” értéket egyre több él jelenik meg a referencia agygráfban. A megdöbbentő észrevétel az, hogy az élek nem véletlenszerűen tűnnek fel, hanem egy kis összefüggő konzervatív gráfból kiindulva egymás után épülve belülről kifelé. Ezen kívül számításokat is végeztünk 395 egyén agygráfjára, felmérve az agyi régiók egyének közötti különbözőségét

High-Resolution Directed Human Connectomes and the Consensus Connectome Dynamics

<div><p>Here we show a method of directing the edges of the connectomes, prepared from HARDI datasets from the human brain. Before the present work, no high-definition directed braingraphs were published, because the tractography methods in use are not capable of assigning directions to the neural tracts discovered. Previous work on the functional connectomes applied low-resolution functional MRI-detected statistical causality for the assignment of directions of connectomes of typically several dozens of vertices. Our method is based on the phenomenon of the “Consensus Connectome Dynamics”, described earlier by our research group. In this contribution, we apply the method to the 423 braingraphs, each with 1015 vertices, computed from the public release of the Human Connectome Project, and we also made the directed connectomes publicly available at the site <a href="http://braingraph.org" target="_blank">http://braingraph.org</a>. We also show the robustness of our edge directing method in four independently chosen connectome datasets: we have found that 86% of the edges, which were present in all four datasets, get the same directions in all datasets; therefore the direction method is robust. While our new edge-directing method still needs more empirical validation, we think that our present contribution opens up new possibilities in the analysis of the high-definition human connectome.</p></div

A comparative study on optimisation of protein extraction methods for Saccharomonospora azurea

To establish the optimal cell disruption and protein extraction protocol for achieving the most efficient whole-cell protein extraction of Saccharomonospora azurea, four commonly used methods (X-Press, bead-vortexing, freezing-throwing and TCA/acetone/phenol extraction) were compared. Total protein content, as well as 1D and 2D SDS-PAGE protein patterns were assayed in the extracts to study the efficacy of these methods. Accordingly, of the four methods the X-Press proved the most effective for all initial weight (maximum 21.523 ± 0.23 mg/ml protein) followed by TCA/acetone/phenol method (maximum 13.682 ± 0.15 mg/ml protein), while the effectiveness of the two other methods were substantially inferior (maximum 3.188 ± 0.03 mg/ml protein). The analysis of protein gels proved that the X-Press method revealed a protein pattern characterised by the presence of the highest number of protein bands (on average of 52 and 385, on 1D and 2D gels, respectively). The TCA/acetone/phenol extraction provided similar effectiveness for only 100-300 mg initial bacteria mass, whereas bead-vortexing produced maximum 35 and 227 separated protein bands, on 1D and 2D gels, respectively. It can be stated that of the four methods the X-Press was the most effective one for all initial weight of bacteria, while the TCA/acetone/phenol method provides interpretable results for the 100-300 mg weight-range of bacteria