Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

iQuantitator: A tool for protein expression inference using iTRAQ

<p>Abstract</p> <p>Background</p> <p>Isobaric Tags for Relative and Absolute Quantitation (iTRAQ™) [Applied Biosystems] have seen increased application in differential protein expression analysis. To facilitate the growing need to analyze iTRAQ data, especially for cases involving multiple iTRAQ experiments, we have developed a modeling approach, statistical methods, and tools for estimating the relative changes in protein expression under various treatments and experimental conditions.</p> <p>Results</p> <p>This modeling approach provides a unified analysis of data from multiple iTRAQ experiments and links the observed quantity (reporter ion peak area) to the experiment design and the calculated quantity of interest (treatment-dependent protein and peptide fold change) through an additive model under log transformation. Others have demonstrated, through a case study, this modeling approach and noted the computational challenges of parameter inference in the unbalanced data set typical of multiple iTRAQ experiments. Here we present the development of an inference approach, based on hierarchical regression with batching of regression coefficients and Markov Chain Monte Carlo (MCMC) methods that overcomes some of these challenges. In addition to our discussion of the underlying method, we also present our implementation of the software, simulation results, experimental results, and sample output from the resulting analysis report.</p> <p>Conclusion</p> <p>iQuantitator's process-based modeling approach overcomes limitations in current methods and allows for application in a variety of experimental designs. Additionally, hypertext-linked documents produced by the tool aid in the interpretation and exploration of results.</p

Machine Learning for Health: Algorithm Auditing & Quality Control

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing

Increasing incidence of adult idiopathic inflammatory myopathies in the City of Salford, UK: A 10-year epidemiological study

Objectives. The aim was to identify and characterize all incident adult cases of idiopathic inflammatory myopathies (IIM) between 1 January 2007 and 31 December 2016 in the City of Salford, UK. / Methods. Adults first diagnosed with IIM within the study period were identified by: a Salford Royal NHS Foundation Trust (SRFT) inpatient episode IIM-specific ICD-10 coding search; all new patient appointments to SRFT neuromuscular outpatient clinics; and all Salford residents enrolled within the UKMYONET study. All patients with definite IIM by the 2017 EULAR/ACR classification criteria were included, as were probable cases if consensus expert opinion agreed. Cases were excluded if < 18 years of age at disease onset, if they did not meet probable criteria or when probable but expert opinion concluded a non-IIM diagnosis. / Results. The multimodal case ascertainment identified 1156 cases which, after review and application of exclusion criteria, resulted in 32 incident cases during the study period. Twenty-three of 32 were female, with a mean age of 58.1 years. The mean incidence of adult IIM was 17.6/1 000 000 person years, and higher for females than for males (25.2 vs 10.0/1 000 000 person years, respectively). A significant incidence increase over time was apparent (13.6 vs 21.4/1 000 000 person years; P=0.032). Using EULAR/ACR classification criteria, the largest IIM subtype (21/32) was PM, followed by DM (8/32), IBM (2/32) and amyopathic DM (1/32). Expert opinion subtype differed from EULAR/ACR classification criteria in 19/32 cases. / Conclusion. The incidence of adult IIM in Salford is 17.6/1 000 000 person years, higher in females, and is increasing over time. Disagreement exists between EULAR/ACR-derived and expert opinionderived IIM subtype assignments

Frequency, mutual exclusivity and clinical associations of myositis autoantibodies in a combined European cohort of idiopathic inflammatory myopathy patients

Objectives: To determine prevalence and co-existence of myositis specific autoantibodies (MSAs) and myositis associated autoantibodies (MAAs) and associated clinical characteristics in a large cohort of idiopathic inflammatory myopathy (IIM) patients. Methods: Adult patients with confirmed IIM recruited to the EuroMyositis registry (n = 1637) from four centres were investigated for the presence of MSAs/MAAs by radiolabelled-immunoprecipitation, with confirmation of anti-MDA5 and anti-NXP2 by ELISA. Clinical associations for each autoantibody were calculated for 1483 patients with a single or no known autoantibody by global linear regression modelling. Results: MSAs/MAAs were found in 61.5% of patients, with 84.7% of autoantibody positive patients having a sole specificity, and only three cases (0.2%) having more than one MSA. The most frequently detected autoantibody was anti-Jo-1 (18.7%), with a further 21 specificities each found in 0.2–7.9% of patients. Autoantibodies to Mi-2, SAE, TIF1, NXP2, MDA5, PMScl and the non-Jo-1 tRNA-synthetases were strongly associated (p < 0.001) with cutaneous involvement. Anti-TIF1 and anti-Mi-2 positive patients had an increased risk of malignancy (OR 4.67 and 2.50 respectively), and anti-SRP patients had a greater likelihood of cardiac involvement (OR 4.15). Interstitial lung disease was strongly associated with the anti-tRNA synthetases, antiMDA5, and anti-U1RNP/Sm. Overlap disease was strongly associated with anti-PMScl, anti-Ku, anti-U1RNP/Sm and anti-Ro60. Absence of MSA/MAA was negatively associated with extra-muscular manifestations. Conclusions: Myositis autoantibodies are present in the majority of patients with IIM and identify distinct clinical subsets. Furthermore, MSAs are nearly always mutually exclusive endorsing their credentials as valuable disease biomarkers

'I'm not going to tell you cos you need to think about this': A conversation analysis study of managing advice resistance and supporting autonomy in undergraduate supervision

This is an accepted manuscript of an article published by Springer in Postdigital Science and Education, available online at: https://doi.org/10.1007/s42438-020-00194-5 The accepted version of the publication may differ from the final published version.This article firstly, critically analyses a face-to-face supervision meeting between an undergraduate and a supervisor, exploring how the supervisor handles the twin strategies of fostering autonomy while managing resistance to advice. Conversation Analysis is used as both a theory and a method, with a focus on the use of accounts to support or resist advice. The main contribution is the demonstration of how both the supervisor and student are jointly responsible for the negotiation of advice, which is recycled and calibrated in response to the student’s resistance. The supervisor defuses complaints by normalising them, and moving his student on to practical solutions, often with humour. He lists his student’s achievements as the foundation on which she can assert agency and build the actions he recommends. Supervisor-student relationships are investigated through the lens of the affective dimensions of learning, to explore how caring or empathy may serve to reduce resistance and make advice more palatable. By juxtaposing physically present supervision with digitally-mediated encounters, while acknowledging their mutual entanglement, the postdigital debate is furthered. In the context of Covid-19, and rapid decisions by universities to bring in digital platforms to capture student-teacher interactions, the analysis presented is in itself an act of resistance against the technical control systems of the academy and algorithmic capitalism