We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance. machine learning | image analysis | software

We present BUSTED, a new approach to identifying gene-wide evidence of episodic positive selection, where the non-synonymous substitution rate is transiently greater than the synonymous rate. BUSTED can be used either on an entire phylogeny (without requiring an a priori hypothesis regarding which branches are under positive selection) or on a pre-specified subset of foreground lineages (if a suitable a priori hypothesis is available). Selection is modeled as varying stochastically over branches and sites, and we propose a computationally inexpensive evidence metric for identifying sites subject to episodic positive selection on any foreground branches. We compare BUSTED with existing models on simulated and empirical data. An implementation is available on www.datamonkey.org/busted, with a widget allowing the interactive specification of foreground branches.

Monoclonal antibody 10-1074 targets the V3 glycan supersite on the HIV-1 envelope protein. It is among the most potent anti-HIV-1 neutralizing antibodies isolated to date. Here we report on its safety and activity in 33 subjects who received a single intravenous infusion of the antibody. 10-1074 was well tolerated with a half-life of 24.0 days in uninfected and 12.8 days in HIV-1-infected subjects. 13 viremic subjects received the highest dose of 30 mg/kg 10-1074. 11 of these participants were 10-1074-sensitive and showed a rapid decline of viremia by a mean of 1.52 log10 copies/ml. Virologic analysis revealed the emergence of multiple independent 10-1074-resistant viruses within the first weeks after infusion. Emerging escape variants were generally resistant to the related V3-specific antibody PGT121, but remained sensitive to antibodies targeting non-overlapping epitopes, such as the anti-CD4 binding site antibodies 3BNC117 and VRC01. The results demonstrate the safety and activity of 10-1074 in humans and support the idea that antibodies targeting the V3 glycan supersite may be useful for treatment and prevention of HIV-1 infection.

SUMMARY The high-mannose patch on HIV Env is a preferred target for broadly neutralizing antibodies (bnAbs), but to date, no vaccination regimen has elicited bnAbs against this region. Here, we present the development of a bnAb lineage targeting the high-mannose patch in an HIV-1 subtype-C-infected donor from sub-Saharan Africa. The Abs first acquired autologous neutralization, then gradually matured to achieve breadth. One Ab neutralized >47% of HIV-1 strains with only ~11% somatic hypermutation and no insertions or deletions. By sequencing autologous env, we determined key residues that triggered the lineage and participated in Ab-Env coevolution. Next-generation sequencing of the Ab repertoire showed an early expansive diversification of the lineage followed by independent maturation of individual limbs, several of them developing notable breadth and potency. Overall, the findings are encouraging from a vaccine standpoint and suggest immunization strategies mimicking the evolution of the entire high-mannose patch and promoting maturation of multiple diverse Ab pathways.

The need to analyze high-dimension biological data is driving the development of new data mining methods. Biclustering algorithms have been successfully applied to gene expression data to discover local patterns, in which a subset of genes exhibit similar expression levels over a subset of conditions. However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. Surveys and comparisons exist in the literature, but because of the large number and variety of biclustering algorithms, they are quickly outdated. In this article we partially address this problem of evaluating the strengths and weaknesses of existing biclustering methods. We used the BiBench package to compare 12 algorithms, many of which were recently published or have not been extensively studied. The algorithms were tested on a suite of synthetic data sets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters and overlapping biclusters. The algorithms were also tested on eight large gene expression data sets obtained from the Gene Expression Omnibus. Gene Ontology enrichment analysis was performed on the resulting biclusters, and the best enrichment terms are reported. Our analyses show that the biclustering method and its parameters should be selected based on the desired model, whether that model allows overlapping biclusters, and its robustness to noise. In addition, we observe that the biclustering algorithms capable of finding more than one model are more successful at capturing biologically relevant clusters.