Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data.

BACKGROUND: Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: first, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Furthermore, existing approaches for signal detection usually rely on strong assumptions concerning the signals properties. RESULTS: In this study, it is shown that locality-sensitive hashing enables signal classification in mass spectrometry raw data at scale. Through appropriate choice of algorithm parameters it is possible to balance false-positive and false-negative rates. On synthetic data, a superior performance compared to an intensity thresholding approach was achieved. Real data could be strongly reduced without losing relevant information. Our implementation scaled out up to 32 threads and supports acceleration by GPUs. CONCLUSIONS: Locality-sensitive hashing is a desirable approach for signal classification in mass spectrometry raw data. AVAILABILITY: Generated data and code are available at https://github.com/hildebrandtlab/mzBucket . Raw data is available at https://zenodo.org/record/5036526 .

NOseq: amplicon sequencing evaluation method for RNA m6A sites after chemical deamination.

Methods for the detection of m6A by RNA-Seq technologies are increasingly sought after. We here present NOseq, a method to detect m6A residues in defined amplicons by virtue of their resistance to chemical deamination, effected by nitrous acid. Partial deamination in NOseq affects all exocyclic amino groups present in nucleobases and thus also changes sequence information. The method uses a mapping algorithm specifically adapted to the sequence degeneration caused by deamination events. Thus, m6A sites with partial modification levels of ∼50% were detected in defined amplicons, and this threshold can be lowered to ∼10% by combination with m6A immunoprecipitation. NOseq faithfully detected known m6A sites in human rRNA, and the long non-coding RNA MALAT1, and positively validated several m6A candidate sites, drawn from miCLIP data with an m6A antibody, in the transcriptome of Drosophila melanogaster. Conceptually related to bisulfite sequencing, NOseq presents a novel amplicon-based sequencing approach for the validation of m6A sites in defined sequences.

Machine learning of reverse transcription signatures of variegated polymerases allows mapping and discrimination of methylated purines in limited transcriptomes.

Reverse transcription (RT) of RNA templates containing RNA modifications leads to synthesis of cDNA containing information on the modification in the form of misincorporation, arrest, or nucleotide skipping events. A compilation of such events from multiple cDNAs represents an RT-signature that is typical for a given modification, but, as we show here, depends also on the reverse transcriptase enzyme. A comparison of 13 different enzymes revealed a range of RT-signatures, with individual enzymes exhibiting average arrest rates between 20 and 75%, as well as average misincorporation rates between 30 and 75% in the read-through cDNA. Using RT-signatures from individual enzymes to train a random forest model as a machine learning regimen for prediction of modifications, we found strongly variegated success rates for the prediction of methylated purines, as exemplified with N1-methyladenosine (m1A). Among the 13 enzymes, a correlation was found between read length, misincorporation, and prediction success. Inversely, low average read length was correlated to high arrest rate and lower prediction success. The three most successful polymerases were then applied to the characterization of RT-signatures of other methylated purines. Guanosines featuring methyl groups on the Watson-Crick face were identified with high confidence, but discrimination between m1G and m22G was only partially successful. In summary, the results suggest that, given sufficient coverage and a set of specifically optimized reaction conditions for reverse transcription, all RNA modifications that impede Watson-Crick bonds can be distinguished by their RT-signature.

Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures.

Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide resolution across the mapped transcriptome. Further downstream modules include tools for visualization, machine learning, and modification calling. From the machine-learning module, quality assessment parameters are provided to gauge the suitability of the initial dataset for effective machine learning and modification calling. This output is useful to improve the experimental parameters for library preparation and sequencing. In summary, the automation of the bioinformatics workflow allows a faster turnaround of the optimization cycles in modification calling.

Median-evoked somatosensory potentials in severe brain injury: does initial loss of cortical potentials exclude recovery?

OBJECTIVE: In patients with severe brain injury (SBI) median-evoked somatosensory potentials (M-SSEP) serve as a prognostic tool. Bilateral loss of cortical responses (BLCR) is usually thought to be a reliable marker of poor prognosis. Prognostic accuracy to predict a poor outcome depends on the cause of coma and is best in hypoxic-ischemic encephalopathy (HIE) reaching almost 100% which is in contrast to patients with other etiologies of coma, especially traumatic brain injury (TBI). Only little evidence exists on the possibility of electrophysiological recovery of BLCR in repeated or serial SSEP-examinations and detailed functional outcome in these cases. METHODS: 28 patients (78.6% male, 21.4% female, mean age 43.1±18.6 years) from our in-patient early (post-acute) neurorehabilitation center with BLCR in their first M-SSEP were re-examined after a mean interval of 66±55.8 days. SBI was caused by different etiologies. We retrospectively analyzed (a) the recovery rate from BLCR in consecutive M-SSEP and (b) the detailed functional outcome of those patients with recovered cortical responses. RESULTS: 14/28 (50%) patients with primarily BLCR showed re-occurrence of cortical potentials, either uni- or bilaterally. Of the 14 patients, one died due to a non-neurological cause. Of the remaining 13 patients 6 - most of them suffering from traumatic brain injury (TBI) - could be transferred to further continuing neurorehabilitation and achieved good functional long-term outcome. BLCR in HIE still had a poor prognosis with none of our patients achieving an outcome better than vegetative state. CONCLUSIONS: Electrophysiological recovery from primarily BLCR seems possible and is accompanied by good functional outcome in a relevant number of patients. Thus caution is warranted in predicting a poor prognosis based predominantly on SSEP, especially in patients with TBI. Focusing SSEP-examination on the early days after severe brain injury and performing only one examination in the case of BLCR may lead to systematic underestimation of the possibility of recovery.

Critical-illness-polyneuropathy as sequelae of severe neurological illness: incidence and impact on ventilator therapy and rehabilitation.

INTRODUCTION: Critical-Illness-Polyneuropathy (CIP) is common in critically ill patients. In contrast to CIP arousing from primary non-neurological causes, studies dealing with CIP following a primary neurological illness are rare. This mono-center, retrospective study was performed to examine a) the incidence of CIP in patients after severe neurological illnesses and b) the impact on ventilator therapy. MATERIAL AND METHODS: Retrospective analysis of all patients, admitted for early (post-acute) neurological rehabilitation following severe neurological diseases between 01.01.2006 and 31.12.2010. Patients routinely underwent standard electrophysiological evaluation (nerve conduction studies of 8 motor and 6 sensory nerves; needle electromyography of 6 muscles). Diagnosis of CIP required a) pathological spontaneous activity in at least two muscles and b) reduced compound muscle activity potentials (CMAP) in at least two motor nerves of different extremities. RESULTS: CIP was diagnosed in 430 of 623 patients (69.0%). Patients with proven CIP required significantly (p < 0.001) longer ventilator therapy (33.1 days) in contrast to patients without CIP (21.5 days). CONCLUSION: CIP is common in patients suffering from primary severe neurological diseases. Due to CIP, duration of ventilator therapy is significantly prolonged. In patients with severe neurological diseases electrophysiology is crucial to establish the correct diagnosis.