<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

Additional file 3: of SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis

An example of SUSHI application to generate TopHat job script. (RB 2 kb

Hypomethylation by MucR is impaired in G1-phase cells.

<p>(A) Methylation percentage of the <i>P169</i>, <i>P1149</i> and <i>P2901</i> sequences in strains in which the methyltransferase CcrM is stabilised. The <i>Hin</i>fI-qPCR analysis indicates that methylation is increased in cells carrying <i>P</i>_<i>lac</i><i>-ccrM</i> or the <i>lon</i> mutation. In the case of the <i>lon</i> mutant, the methylation of <i>P169</i>, <i>P1149</i> and <i>P2901</i> is not affected by increased levels of MucR1 [<i>R1</i>: <i>P</i>_<i>van</i><i>-mucR1</i>, <i>R1 long</i>: N-terminally extended dominant-negative MucR1 variant expressed from P_<i>van</i> on pMT335]. (B) Methylation percentage of the <i>P169</i>, <i>P1149</i> and <i>P2901</i> sequences in <i>WT</i> cells that constitutively express <i>ccrM</i> or heterologous GANTC-methylases from P_<i>van</i> on pMT335 (TA, <i>Thermoplasma acidophilum</i>; HP, <i>Helicobacter pylori</i>; <i>hinf</i>, <i>Haemophilus influenzae</i>). (C) Immunoblot showing the steady-state levels of CcrM, MucR1 and MucR2 in <i>WT</i> cells as well as the <i>P</i>_<i>lac</i><i>-ccrM</i>, <i>lon</i> and Δ<i>mucR1/2</i> strains. Molecular size standards are indicated on the right as blue lines with the corresponding values in kDa. (D) Genome-wide occupancy of MucR1 in synchronised <i>WT</i> cells at four different time points as determined by ChIP-Exo and super-resolution bioinformatic analysis: swarmer (T10), stalked (T40), early pre-divisional (T70) and late pre-divisional cells (T100). The <i>x</i> axis represents the nucleotide position on the genome, whereas the <i>y</i> axis shows the enrichment ratio (E.R.) for each promoter region as reported in the <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006499#pgen.1006499.s009" target="_blank">S4 Table</a> (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006499#sec010" target="_blank">Methods</a> for a detailed description). (E) Heat map of the enrichment ratios of selected loci (those that contain hypomethylated GANTCs occluded by MucR, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006499#pgen.1006499.g002" target="_blank">Fig 2</a>) at the four time points after synchronization. The <i>pilA</i> locus is shown as comparison as it is a well-characterized target of MucR [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006499#pgen.1006499.ref017" target="_blank">17</a>]. The heat map shows that MucR1 is constitutively associated with these loci.</p

MucR and methylation by CcrM regulate transcription from target promoters.

<p>Beta-galactosidase activity of P<i>169</i> (<i>WT</i> promoter) and P<i>169*</i> (with all GANTCs mutated to GTNTCs) (A), P<i>2901</i> (<i>WT</i> promoter) and P<i>2901*</i> (with the two GANTCs mutated to GTNTCs) (B) and P<i>1149</i> (C) in <i>WT</i> and Δ<i>mucR1/2</i> cells. Mutation of MucR1/2 increases expression from P<i>169</i>-, P<i>2901</i>- and P<i>1149-lacZ</i> independently from the presence of GANTCs. Values are expressed as percentages (activity of <i>WT</i> promoter in <i>WT</i> cells set at 100%). (D) Beta-galactosidase activity of P<i>169</i>-, P<i>1149</i>-, P<i>2901-lacZ</i> promoter probe constructs and two MucR-dependent control promoter reporters (P_<i>hvyA</i>-<i>lacZ</i> and P_<i>pilA</i><i>-lacZ</i>) in <i>WT</i> and cells that constitutively express <i>ccrM</i> (ccrM::P_<i>lac</i><i>-ccrM</i> or Δ<i>lon</i>::Ω). Methylation of the target promoters by CcrM increases the LacZ activity. Values are expressed as percentages (activity in <i>WT</i> cells set at 100%). (E) Beta-galactosidase activity of P<i>169</i>-, P<i>1149</i>- and P<i>2901-lacZ</i> in <i>WT</i> cells that constitutively express <i>ccrM</i> or a heterologous GANTC-methylase from <i>T</i>. <i>acidophilum</i> (TA) on plasmid under control of <i>P</i>_<i>van</i>. Values are expressed as percentages (activity in <i>WT</i> carrying the empty vector set at 100%). (F) Beta-galactosidase activity of P<i>169</i>-, P<i>1149</i>- and P<i>2901-lacZ</i> in <i>WT</i> and Δ<i>mucR1/2</i> cells that constitutively express <i>ccrM</i> (ccrM::P_<i>lac</i><i>-ccrM</i>). Values are expressed as percentages (activity in <i>WT</i> cells set at 100%).</p

REC-Seq in <i>WT</i> and Δ<i>mucR1/2</i>.

<p>GANTC sites with the highest (top 50) REC-Seq score in <i>WT C</i>. <i>crescentus</i> are listed. The complete list of REC-Seq data for both <i>WT</i> and Δ<i>mucR1/2</i> strains is reported in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006499#pgen.1006499.s006" target="_blank">S1 Table</a>.</p

MucR occludes specific GANTC sites from methylation.

<p>(A) Schematic of the loci carrying hypomethylated GANTCs occluded by MucR. The position of the hypomethylated GANTCs identified by Kozdon et al. [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006499#pgen.1006499.ref012" target="_blank">12</a>] is indicated by purple asterisks. Red lines represent the occupancy of MucR1 and the values (x10⁴ <i>per-base</i> coverage) calculated by the super-resolution bioinformatic approach represent the average of the four time points (T10, T40, T70 and T100, as described in the <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006499#sec010" target="_blank">Methods</a>). The MucR-dependent transcription start sites, determined by TSS-EMOTE, are indicated by black (sense) and green (antisense) arrows. Dashed arrows indicate transcription start sites found in both <i>WT</i> and Δ<i>mucR1/2</i> strains (<i>CCNA_01083</i>-<i>CCNA_01084</i>) or down-regulated in the Δ<i>mucR1/2</i> strain compared to the <i>WT</i> (promoter of <i>CCNA_02831</i>). (B) Methylation percentage of the loci shown in panel A in the <i>WT</i> and Δ<i>mucR1/2</i> strains, as determined by <i>Hin</i>fI-qPCR.</p

Hypomethylation control by MucR is conserved in α-proteobacteria.

<p>(A) Schematic of the two MucR-dependent hypomethylated loci (<i>SMa1635</i> and <i>SMa2245</i>) identified by SMRT-sequencing in the in <i>S</i>. <i>meliloti WT</i> genome. Position of the hypomethylated GANTCs is indicated by purple asterisks. The blue arrows indicate the DNA fragments cloned for LacZ promoter probe assays. (B) <i>Hin</i>fI-qPCR analysis showing that <i>SMa1635</i> and <i>SMa2245</i> are hypomethylated in <i>S</i>. <i>meliloti WT</i> cells compared to <i>mucR</i>::<i>Tn</i> cells. (C) Constitutive expression of <i>ccrM</i>^<i>Cc</i> from P_<i>lac</i> on pSRK [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006499#pgen.1006499.ref047" target="_blank">47</a>] in <i>S</i>. <i>meliloti WT</i> cells increases the methylation percentage of <i>SMa1635</i> and <i>SMa2245</i>, indicating that hypomethylation of GANTCs by MucR is also impaired in <i>S</i>. <i>meliloti</i> G1-phase cells. (D) MucR occupancy at <i>SMa1635</i>, <i>SMa2245</i> and <i>SMc1552</i> (control) in <i>WT</i> and <i>mucR</i>::<i>Tn S</i>. <i>meliloti</i> cells, as determined by qChIP using antibodies to <i>C</i>. <i>crescentus</i> MucR2. <i>SMa1635</i> and <i>SMa2245</i> are bound by <i>S</i>. <i>meliloti</i> MucR, which suggests that hypomethylation of GANTCs at these loci is directly due to occlusion by MucR. (E) Beta-galactosidase activity of P<i>a1635–lacZ</i> and P<i>a2245–lacZ</i> in <i>S</i>. <i>meliloti</i> (fragments indicated by blue arrows in panel <b>A</b>). Both DNA fragments show a promoter activity that is strongly de-repressed in <i>mucR</i>::<i>Tn</i> cells compared to the <i>WT</i> strain. Values are expressed as percentages (activity in <i>WT</i> cells set at 100%). (F) Beta-galactosidase activity of P<i>a1635</i> and P<i>a2245</i> in <i>C</i>. <i>crescentus WT</i> and Δ<i>mucR1/2</i> cells expressing <i>mucR</i>^<i>Sm</i>. Expression of <i>mucR</i>^<i>Sm</i> from P_<i>van</i> on pMT335 decreases beta-galactosidase activity of P<i>a1635</i> and P<i>a2245</i>. Values are expressed as percentages (activity in <i>WT</i> cells carrying the empty vector set at 100%). (G) Beta-galactosidase activity of P<i>a1635</i> and P<i>a2245</i> in <i>C</i>. <i>crescentus WT</i>, <i>P</i>_<i>lac</i>-<i>ccrM</i> or <i>lon</i> cells. Values are expressed as percentages (activity in <i>WT</i> cells set at 100%).</p

Cell cycle and environmental signals affect methylation patterns.

<p>(A) Competition between CtrA and CcrM. Schematic of the synthetic promoter carrying an attenuated <i>E</i>. <i>coli</i> phage T5 promoter followed by three GANTCs (purple asterisks) overlapping two CtrA-boxes (in yellow). <i>Hin</i>fI-qPCR analysis shows that this sequence is hypomethylated in <i>WT</i> cells, whereas constitutive expression of <i>ccrM</i> (P_<i>lac</i><i>-ccrM</i>) increases the methylation percentage. This indicates that DNA-binding proteins other than MucR can also occlude GANTCs from methylation. (B) Methylation percentage of P<i>169</i> and P<i>1149</i> in phosphate-limiting conditions compared to rich medium (PYE), determined by <i>Hin</i>fI-qPCR analysis. Phosphate starvation (6h) significantly increases the methylation level of P<i>169</i> but not P<i>1149</i>. (C) Beta-galactosidase activity of P<i>169–lacZ</i> and <i>P169*–lacZ</i> (GANTCs mutated to GTNTCs as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006499#pgen.1006499.g004" target="_blank">Fig 4A</a>) in <i>WT</i> and Δ<i>mucR1/2</i> cells in rich medium and phosphate-limiting conditions. Phosphate starvation induces transcription from P<i>169–lacZ</i> and <i>P169*–lacZ</i> independently from the presence of MucR1/2. Values are expressed as percentages (activity in <i>WT</i> cells grown in PYE set at 100%). (D) Beta-galactosidase activity of P<i>1149–lacZ</i> in <i>WT</i> and Δ<i>mucR1/2</i> cells in rich medium and phosphate-limiting conditions. Phosphate starvation does not significantly affect the activity of P<i>1149–lacZ</i>. Values are expressed as percentages (activity in <i>WT</i> cells grown in PYE set at 100%).</p

GC Content of CDSs That Are Universally Present or Variable within <i>H. pylori</i>

<p>CDSs were binned according to GC content in steps of 2% (24–26, 26–28, etc.). Top: Fraction of all CDSs within a bin that are variable. Bottom: Fraction of universally present (<i>n</i> = 1,150) or variable (<i>n</i> = 499) CDSs by GC content. One universally present CDS with a GC content of 62% (HP0359) has been excluded from the figure.</p

Extrapolated Number of Universally Present CDSs in <i>H. pylori</i>

<p>The fraction of CDSs present in a sample of strains (“common CDSs”) was calculated on random samples of one to 56 strains taken without replacement. Mean fractions of common CDSs were calculated from 100 iterations of this sampling procedure. The graph shows the results of fitting an exponential decay model to these calculations, in which y0 approaches the minimum number of universally common CDSs at infinity (0.674 × 1,649 CDSs = 1,111 universally present CDSs).</p