Dia tools

Q-value filtering applied when generating the library. DIA-NN will search these decoys in addition to the regular decoys it generates. Therefore, if the fraction of decoys is in the range of tens of percent (i.e. library FDR is >= 0.1), this will make the resulting DIA-NN's FDR estimates too conservative, which is fine for most experiments. Nevertheless, this ensures correct FDR control even with libraries filtered at >= 0.5 q-value.Q-values for all entries, target and decoy. While this is not essential to ensure FDR control provided decoys are included, DIA-NN's algorithms use these q-values to improve identification performance. In case decoys are not provided, including q-values may, in most cases, largely ensure correct FDR control by itself. It is, therefore, always recommended.The numeric columns in DIA-NN's .parquet libraries are of types INT64 and FLOAT, other types should not be used.For third-party downstream tools, it may be useful to have DIA-NN also export the decoy identifications using --report-decoys.QuantificationDIA-NN implements Legacy (direct) and QuantUMS quantification modes. The default QuantUMS (high-precision) is recommended in most cases. QuantUMS enables machine learning-optimised relative quantification of precursors and proteins, maximising precision while in many cases eliminating any ratio compression, see Key publications. DIA-NN 2.0 has a much improved set of QuantUMS algorithms, compared to our original preprint.Note that if you are analysing with an empirical library, you can quickly generate reports corresponding to different quantification modes with Reuse .quant files. This can also be done just for a subset of raw files, i.e. if you have analysed also blanks and now wish to exclude them.We have observed that:QuantUMS performance is largely unchanged regardless of the experiment size, i.e. it is suitable for large experiments.QuantUMS works well also on experiments which include very different sample amounts (tested with 10x range across different samples). Note, however, that in

In known molar amounts, to quantify their endogenous unlabelled counterparts, using the fact that the ionisation efficiency does not depend on the isotopic composition. Alternatively, absolute quantities of proteins can be estimated with approaches such as IBAQ, although such estimates tend to be of fairly low precision.Of note, by default DIA-NN reports 'normalised' quantities. This means it makes the best effort to correct for any kind of technical variation in the data that affects all precursors at the same time, such as any kind of pipetting imprecision in sample preparation or different amounts of starting sample material. Indeed, consider a situation when one compares the proteomes of two single cells. If one cell is twice larger, then without normalisation almost every protein will appear differentially expressed. However, such a conclusion is of hardly any biological interest, hence the need for normalisation. Nevertheless, DIA-NN can also provide raw non-normalised intensities, if these are needed given a particular experiment design.Notes on terminology:'peptide' can be used to refer also to precursor ions;'FDR' can be used interchangeably with 'q-value', although these are, strictly-speaking, not equivalent;the term 'library-free analysis' usually refers to using DIA-NN with a predicted spectral library generated from the whole sequence database, that is without an empirical library.Use of R or Python. We have designed DIA-NN in such a way that the whole workflow requires zero bioinformatics expertise, i.e. indeed all downstream statistical analyses and visualisation can be carried out using MS Excel or similar software as well as dedicated visual tools for proteomics data analysis. However, once you have analysed your data and are now preparing a publication, we do recommend learning some basics of data analysis in R. R is a 'programming language' which nevertheless is very easy to master as coding data processing in R is in a sense

Making them unreliable for quantification. Therefore, if you require extra confidence in specific identifications, it does make sense to apply a PEP filter, respectively at the precursor (PEP) or protein (PG.PEP) level.Q: What is normalisation and how does it work?A: The purpose of normalisation is to cancel out some of the undesirable effects which can occur during sample preparation and acquisition. For example, pipetting errors can result in different total amounts being injected, and thus signals of different intensity being recorded by the mass spec. The basic global normalisation addresses this. It should in general be used under the following assumption: when only considering 'biological' variability of interest, most of the peptides are not differentialy abundant, or the numbers of upregulated / downregulated (in comparison to experiment-wide averages) peptides in each sample are about the same. If this condition is not satisfied (e.g. when analysing fractions obtained with some separation technique, like SEC, for instance), then normalisation should not be used.DIA-NN also supports a more advanced normalisation method, which is enabled by default: RT-dependent normalisation. Sometimes, undesirable technical effects can lead to some samples having either hydrophilic or hydrophobic peptides depleted, relative to other samples. These effects are corrected by RT-dependent normalisation. Potentially it might also correct for spray stability issues.The choice of the normalisation strategy in DIA-NN determines the way .Normalised and .MaxLFQ quantities (in the main report), as well as values in output matrices are calculated. So if normalisation is disabled, these quantities are actually non-normalised.Q: How to compare the identification performance of two DIA software tools?A: This is often tricky because FDR estimates are never precise. For example, recently we have benchmarked (see Supplementary Figure S3 therein) DIA-NN on data acquired with different chromatographic gradients, and what we observed is that although DIA-NN is always somewhat conservative

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