Daniel J. Geiszler
Dec 1, 2020; 0:TIR120.002216v1-mcp.TIR120.002216
Technological Innovation and Resources

Open searching has proven to be an effective strategy for identifying both known and unknown modifications in shotgun proteomics experiments. Rather than being limited to a small set of user-specified modifications, open searches identify peptides with any mass shift that may correspond to a single modification or a combination of several modifications. Here we present PTM-Shepherd, a bioinformatics tool that automates characterization of PTM profiles detected in open searches based on attributes such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multi-experiment comparisons for studying changes in modification profiles, e.g. in data generated in different laboratories or under different conditions. We demonstrate how PTM-Shepherd improves the analysis of data from formalin-fixed paraffin-embedded samples, detects extreme underalkylation of cysteine in some datasets, discovers an artefactual modification introduced during peptide synthesis, and uncovers site-specific biases in sample preparation artifacts in a multi-center proteomics profiling study.

My employer is used to meetings in person, but during the CoronApocalypse has begun taking meetings in Zoom like many others. I just got through with a 2-hour (actually 1:51) meeting and my employer would like a summary -- not a transcription, but a summary -- of what was said. The idea is that they would be able to look at this summary and recall the flow of the discussion, what people were communicating at different points, we do not need word-for-word.

Challenges: (1) Recorded via a voice dictation stood up next to a laptop.
(2) 4 voices doing most of the talking, with a few others occasionally coming in. Unfortunately the main voices interrupt and try to talk over each other, which went poorly over Zoom. Fortunately the voices are distinct, and I can let you know who's who so you can label them appropriately.
(3) Some technical jargon related to construction, land/soil shifting and the law regarding buildings.

Let me know your estimate for this work, and when we reach agreement I can send you an 80MB MP3 file through Dropbox or Google Drive.

Deadline: done by this Friday evening

We also have another meeting with the same people scheduled for next week.

Adriaan Sticker
Apr 22, 2020; 0:RA119.001624v1-mcp.RA119.001624
Research

Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis due to peptide-specific effects and context dependent missingness of peptide intensities. Peptide-based workflows, like MSqRob, test for DE directly from peptide intensities and outperform summarization methods which first aggregate MS1 peptide intensities to protein intensities before DE analysis. However, these methods are computationally expensive, often hard to understand for the non-specialised end-user, and do not provide protein summaries, which are important for visualisation or downstream processing. In this work, we therefore evaluate state-of-the-art summarization strategies using a benchmark spike-in dataset and discuss why and when these fail compared to the state-of-the-art peptide based model, MSqRob. Based on this evaluation, we propose a novel summarization strategy, MSqRobSum, which estimates MSqRob’s model parameters in a two-stage procedure circumventing the drawbacks of peptide-based workflows. MSqRobSum maintains MSqRob’s superior performance, while providing useful protein expression summaries for plotting and downstream analysis. Summarising peptide to protein intensities considerably reduces the computational complexity, the memory footprint and the model complexity, and makes it easier to disseminate DE inferred on protein summaries. Moreover, MSqRobSum provides a highly modular analysis framework, which provides researchers with full flexibility to develop data analysis workflows tailored towards their specific applications.

Text summarization refers to the process that generates a shorter form of text from the source document preserving salient information. Recently, many models for text summarization have been proposed. Most of those models were evaluated using recall-oriented understudy for gisting evaluation (ROUGE) scores. However, as ROUGE scores are computed based on n-gram overlap, they do not reflect semantic meaning correspondences between generated and reference summaries. Because Korean is an agglutinative language that combines various morphemes into a word that express several meanings, ROUGE is not suitable for Korean summarization. In this paper, we propose evaluation metrics that reflect semantic meanings of a reference summary and the original document, Reference and Document Aware Semantic Score (RDASS). We then propose a method for improving the correlation of the metrics with human judgment. Evaluation results show that the correlation with human judgment is significantly higher for our evaluation metrics than for ROUGE scores.