<i>prolfqua</i>: A Comprehensive <i>R</i>‑Package for Proteomics Differential Expression Analysis

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics

Proportion of detected histone peptides in the adult mouse brain.

<p><b>A</b>) Workflow for the isolation and analysis of long histone peptides from the mouse brain. <b>B</b>) Number of peptides identified for each histone subtype, in brackets the number of unique peptides identified and typical sequence coverage observed.</p

Summary of overrepresented motifs at PTM sites.

<p>A total of ten motifs were detected, flanking either acetylation, methylation or phosphorylation sites. For each motif, bibliographic references for the same or similar motifs are listed, as well as whether it is known to function as a binding motif. Unknown motifs were novel at the time of writing. Many of the identified motifs are novel and distinct from human motifs in the human protein reference database (HPRD). In support of our dataset many of the sites also matched known motifs for the enzymes that catalyse these PTMs, and/or known binding motifs that require modified residues.</p

A,B) Mass spectra of identified endogenous peptides with lysine propionylation and butyrylation and their synthetic counterparts.

<p>Major peaks are labelled in the mass spectra and the fragment ions indicated in the peptide sequence using standard Mascot nomenclature <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036980#pone.0036980-Roepstorff1" target="_blank">[56]</a>. <b>A</b>) A novel site of lysine butyrylation on residue K95 of H2A. <b>B</b>) A novel site of lysine propionylation on residue K95 of H2A. <b>C</b>) Highly modified peptides that were detected using ETD-MS/MS included the N-terminal peptide from H4 ac-SGRGKacGGKacGLGKacGGAKacRHRKme2VLR, which contains 5 sites of acetylation and 1 site of dimethylation <b>E</b>) A novel site of lysine crotonylation on residue K108 of H2B.</p

All novel histone PTMs.

<p>Summary of all novel PTMs identified on H1 (<b>A</b>), H2A (<b>B</b>), H2B (<b>C</b>), H3 (<b>D</b>) <b>and H4</b> (<b>E</b>). Sites of PTMs are indicated by A for acetylation, B for butyrylation, Cr for crotonylation, Me1, Me2 and Me3 for mono-, di- and trimethylation, P for phosphorylation and Pr for propionylation. Residues are numbered starting with the first residue after the cleaved methionine. Canonical H1, H2A, H2B and H3 histones are shown which represent sequences common across all subtypes.</p

Combinatorial patterns on H2A and H2B.

<p>Summary of N-terminal H2B and H2A combinatorial codes identified using ETD-MS. Each line represents an individual H2B_1–25 (<b>A</b>) or H2A_1–41 (<b>C</b>) peptide. Probability of co-occurrence of individual PTMs on H2B_1–25 determined by an association rule data-mining algorithm (<b>B</b>). The condition (left rows) is when a specific PTM is observed on H2A/H2B, and the outcome (top columns) is the probability (indicated by a heat plot) that a second or several PTM(s) are observed at the same time on the same histone molecule. Diagram depicting the relationship between three PTMs on H2A_1–41 (<b>D</b>). N-term Ac, K5ac and R3Me3 were either mutually exclusive or always seen in combination. The frequency of each PTM is indicated by the % above the circle, connections indicate the derived rule and its % occurrence. For instance, when K5ac was present, N-term ac was also observed in 100% of cases (connected by arrow), but R3me3 was never observed. When R3me3 was observed, N-term acetylation or K5ac was never observed (100% of cases), suggesting mutual exclusion of N-term acetylation and R3me3, potentially due to steric hindrance or conformational changes induced by each PTM.</p

Multiple PTMs occur in distinct combinations on H4.

<p><b>A</b>) Table depicting each of the 304 combinatorial codes identified on H4_1–24 by ETD-MS. The number of each residue carrying a PTM is indicated at the top and each line represents an individual peptide. Probability of co-occurrence of (<b>B</b>) individual PTMs and (<b>C</b>) individual PTMs with groups of PTMs, on H4_1–23 determined by an association rule data-mining algorithm. The condition (left rows) is when a specific PTM is observed on H4, and the outcome (top columns) is the probability (indicated by a heat plot) that a second or several PTM(s) are observed at the same time on the same histone molecule.</p

Summary of PTM abundance.

<p>The site, type and level of modification site occupancy of each PTM is indicated for each histone. For each PTM, the level of modification site occupancy is calculated by dividing the number of instances that a PTM was detected by the number of instances that a given amino acid was observed, providing an estimation of the relative abundance of each PTM. Sites of phosphorylation, which were detected only in IMAC/TiO₂ enriched fractions, are not listed. Residues are numbered starting with the first residue after the cleaved methionine. Canonical H1 (<b>1A</b>), H2A (<b>1B</b>), H2B (<b>1C</b>), <b>H3</b> (<b>1D</b>), and H4 (<b>1E</b>) histones are shown which represent sequences common across all subtypes.</p

Unit Cell Structure of Crystal Polytypes in InAs and InSb Nanowires

The atomic distances in hexagonal polytypes of III−V compound semiconductors differ from the values expected from simply a change of the stacking sequence of (111) lattice planes. While these changes were difficult to quantify so far, we accurately determine the lattice parameters of zinc blende, wurtzite, and 4H polytypes for InAs and InSb nanowires, using X-ray diffraction and transmission electron microscopy. The results are compared to density functional theory calculations. Experiment and theory show that the occurrence of hexagonal bilayers tends to stretch the distances of atomic layers parallel to the <i>c</i> axis and to reduce the in-plane distances compared to those in zinc blende. The change of the lattice parameters scales linearly with the hexagonality of the polytype, defined as the fraction of bilayers with hexagonal character within one unit cell