Streaming histogram sketching for rapid microbiome analytics

Background: The growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research, allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time. To address this need, we propose a new method for the compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching and classification of microbiome samples in near real time. Results: We apply streaming histogram sketching to microbiome samples as a form of dimensionality reduction, creating a compressed ‘histosketch’ that can efficiently represent microbiome k-mer spectra. Using public microbiome datasets, we show that histosketches can be clustered by sample type using the pairwise Jaccard similarity estimation, consequently allowing for rapid microbiome similarity searches via a locality sensitive hashing indexing scheme. Furthermore, we use a ‘real life’ example to show that histosketches can train machine learning classifiers to accurately label microbiome samples. Specifically, using a collection of 108 novel microbiome samples from a cohort of premature neonates, we trained and tested a random forest classifier that could accurately predict whether the neonate had received antibiotic treatment (97% accuracy, 96% precision) and could subsequently be used to classify microbiome data streams in less than 3 s. Conclusions: Our method offers a new approach to rapidly process microbiome data streams, allowing samples to be rapidly clustered, indexed and classified. We also provide our implementation, Histosketching Using Little K-mers (HULK), which can histosketch a typical 2 GB microbiome in 50 s on a standard laptop using four cores, with the sketch occupying 3000 bytes of disk space

Subcortical nuclei volumes in suicidal behavior: nucleus accumbens may modulate the lethality of acts.

International audiencePreviously, studies have demonstrated cortical impairments in those who complete or attempt suicide. Subcortical nuclei have less often been implicated in the suicidal vulnerability. In the present study, we investigated, with a specific design in a large population, variations in the volume of subcortical structures in patients with mood disorders who have attempted suicide. We recruited 253 participants: 73 suicide attempters with a past history of both mood disorders and suicidal act, 89 patient controls with a past history of mood disorders but no history of suicidal act, and 91 healthy controls. We collected 1.5 T magnetic resonance imaging data from the caudate, pallidum, putamen, nucleus accumbens, hippocampus, amygdala, ventral diencephalon, and thalamus. Surface-based morphometry (Freesurfer) analysis was used to comprehensively evaluate gray matter volumes. In comparison to controls, suicide attempters showed no difference in subcortical volumes when controlled for intracranial volume. However, within attempters negative correlations between the left (r = -0.35, p = 0.002), and right (r = -0.41, p < 0.0005) nucleus accumbens volumes and the lethality of the last suicidal act were found. Our study found no differences in the volume of eight subcortical nuclei between suicide attempters and controls, suggesting a lack of association between these regions and suicidal behavior in general. However, individual variations in nucleus accumbens structure and functioning may modulate the lethality of suicidal acts during a suicidal crisis. The known role of nucleus accumbens in action selection toward goals determined by the prefrontal cortex, decision-making or mental pain processing are hypothesized to be potential explanations