The DNA60IFX contest

We present the full story of Genome Biology's recent DNA60IFX contest, as told by the curators and winner of what turned out to be a memorable and hotly contested bioinformatics challenge. Full solutions, including scripts, are available at http://genomebiology.com/about/update/DNA60_ANSWER

Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research.</p> <p>Results</p> <p>Upon close survey, I realized that many of these new methods were ill-tested. In addition, newer methods were often published without performance comparison with previous ones. Thus, it is not clear how good they are and whether there are significant performance differences among them. In this study, I have implemented and thoroughly tested 4 different methods on large-scale, non-redundant data sets. It reveals several important points. First, significant performance differences are noted among different methods. Second, data sets typically used for training prediction methods appear significantly biased, limiting the general applicability of prediction methods trained with them. Third, there is still ample room for further developments. In addition, my analysis illustrates the importance of complementary performance measures coupled with right-sized data sets for meaningful benchmark tests.</p> <p>Conclusions</p> <p>The current study reveals the potentials and limits of the new category of sequence-based protein-protein interaction prediction methods, which in turn provides a firm ground for future endeavours in this important area of contemporary bioinformatics.</p

Is there a common water-activity limit for the three domains of life?

Archaea and Bacteria constitute a majority of life systems on Earth but have long been considered inferior to Eukarya in terms of solute tolerance. Whereas the most halophilic prokaryotes are known for an ability to multiply at saturated NaCl (water activity (a w) 0.755) some xerophilic fungi can germinate, usually at high-sugar concentrations, at values as low as 0.650-0.605 a w. Here, we present evidence that halophilic prokayotes can grow down to water activities of <0.755 for Halanaerobium lacusrosei (0.748), Halobacterium strain 004.1 (0.728), Halobacterium sp. NRC-1 and Halococcus morrhuae (0.717), Haloquadratum walsbyi (0.709), Halococcus salifodinae (0.693), Halobacterium noricense (0.687), Natrinema pallidum (0.681) and haloarchaeal strains GN-2 and GN-5 (0.635 a w). Furthermore, extrapolation of growth curves (prone to giving conservative estimates) indicated theoretical minima down to 0.611 a w for extreme, obligately halophilic Archaea and Bacteria. These were compared with minima for the most solute-tolerant Bacteria in high-sugar (or other non-saline) media (Mycobacterium spp., Tetragenococcus halophilus, Saccharibacter floricola, Staphylococcus aureus and so on) and eukaryotic microbes in saline (Wallemia spp., Basipetospora halophila, Dunaliella spp. and so on) and high-sugar substrates (for example, Xeromyces bisporus, Zygosaccharomyces rouxii, Aspergillus and Eurotium spp.). We also manipulated the balance of chaotropic and kosmotropic stressors for the extreme, xerophilic fungi Aspergillus penicilloides and X. bisporus and, via this approach, their established water-activity limits for mycelial growth (∼0.65) were reduced to 0.640. Furthermore, extrapolations indicated theoretical limits of 0.632 and 0.636 a w for A. penicilloides and X. bisporus, respectively. Collectively, these findings suggest that there is a common water-activity limit that is determined by physicochemical constraints for the three domains of life

Untersuchung hyperfunktioneller Dysphonien auf Basis des Einschwingens der Stimmlippen

Stimmstörungen können funktionelle oder organische Ursachen haben. Funktionelle Stimmstörungen gehören zu den häufigsten Stimmstörungen. Sie sind Krankheiten, die durch die Störung des Stimmklangs und der stimmlichen Leistungsfähigkeit gekennzeichnet sind, ohne dass sich klinisch primär organische Veränderungen der an der Stimmgebung beteiligten Strukturen zeigen. Sie können Folge falschen Stimmgebrauchs, von Überlastung oder Umwelteinflüssen sein. Es wird zwischen hyper- und hypofunktionellen Störungen unterschieden. Das Einschwingverhalten kann mit Hilfe der Hochgeschwindigkeits-Videotechnik (HVT) untersucht werden. Unter Zuhilfenahme eines Modells war es möglich ein objektives Diagnoseverfahren zu entwickeln, welches die Anforderungen des klinischen Alltags weitestgehend erfüllt. Normale Stimmen und hypofunktionelle Stimmstörungen unterscheiden sich in den beiden untersuchten Hauptparametern signifikant . Breite Streuungen bezüglich der Hauptparameter zeigen hyperfunktionelle Dysphonien. Die unterschiedliche Einbeziehung supraglottischer Strukturen scheint hier eine wesentliche Rolle zu spielen. Es werden die Ergebnisse einer Untersuchung vorgestellt, die nach einem Zusammenhang zwischen dem Einfluss der supraglottischen Strukturen und dem Einschwingverhalten der Stimmlippen sucht. In die Studie wurden 80 Patientinnen einbezogen

Deep Sequencing Reveals Mutagenic Effects of Ribavirin During Monotherapy of {HCV} Genotype 1-infected Patients

The preeminent mode of action of the broad-spectrum antiviral nucleoside ribavirin in the therapy of chronic hepatitis C is currently unresolved. Particularly under contest are possible mutagenic effects of ribavirin that may lead to viral extinction by lethal mutagenesis of the hepatitis C virus (HCV) genome. We applied ultradeep sequencing to determine ribavirin-induced sequence changes in the HCV coding region (nucleotides [nt] 330 to 9351) of patients treated with 6-week ribavirin monotherapy (n = 6) in comparison to placebo (n = 6). Baseline HCV RNA levels maximally declined on average by −0.8 or −0.1 log(10) IU/ml in ribavirin- versus placebo-treated patients. No general increase in rates of nucleotide substitutions in ribavirin-treated patients was observed. However, more HCV genome positions with high G-to-A and C-to-U transition rates were detected between baseline and treatment week 6 in ribavirin-treated patients in comparison to placebo-treated patients (rate of 0.0041 transitions per base pair versus rate of 0.0022 transitions per base pair; P = 0.049). Similarly, the sensitive detection of low-frequency minority variants by statistical filtering indicated significantly more positions with G-to-A and C-to-U transitions in ribavirin-treated patients than in placebo-treated patients (rate of 0.0331 transitions versus rate of 0.0186 transitions per G/C-containing position at baseline; P = 0.018). In contrast, non-ribavirin-associated A-to-G and U-to-C transitions were not enriched in the ribavirin group (P = 0.152). We conclude that ribavirin exerts a mutagenic effect on the virus in patients with chronic hepatitis C by facilitating G-to-A and C-to-U nucleotide transitions

Common cancer biomarkers of breast and ovarian types identified through artificial intelligence

Biomarkers can offer great promise for improving prevention and treatment of complex diseases such as cancer, cardiovascular diseases, and diabetes. These can be used as either diagnostic or predictive or as prognostic biomarkers. The revolution brought about in biological big data analytics by artificial intelligence (AI) has the potential to identify a broader range of genetic differences and support the generation of more robust biomarkers in medicine. AI is invigorating biomarker research on various fronts, right from the cataloguing of key mutations driving the complex diseases like cancer to the elucidation of molecular networks underlying diseases. In this study, we have explored the potential of AI through machine learning approaches to propose that these methods can act as recommendation systems to sort and prioritize important genes and finally predict the presence of specific biomarkers. Essentially, we have utilized microarray datasets from open‐source databases, like GEO, for breast, lung, colon, and ovarian cancer. In this context, different clustering analyses like hierarchical and k‐means along with random forest algorithm have been utilized to classify important genes from a pool of several thousand genes. To this end, network centrality and pathway analysis have been implemented to identify the most potential target as CREB1