Nn search

Author: n | 2025-04-24

Fast NN search performance and has a fair comparison with hnswlib. no change to hnsw indexing and NN search. our NN search = hnsw layer0's NN search random seeds.

Course Project: NN Search on k-NN Graph and the

MS2 mass accuracy to N ppm--mass-acc-cal [N] sets the mass accuracy used during the calibration phase of the search to N ppm (default is 100 ppm, which is adjusted automatically to lower values based on the data)--mass-acc-ms1 [N] sets the MS1 mass accuracy to N ppm--matrices output quantities matrices--matrix-qvalue [X] sets the q-value used to filter the output matrices--matrix-spec-q [X] run-specific protein q-value filtering will be used, in addition to the global q-value filtering, when saving protein matrices. The ability to filter based on run-specific protein q-values, which allows to generate highly reliable data, is one of the advantages of DIA-NN--max-pep-len [N] sets the maximum precursor length for the in silico library generation or library-free search--max-pr-charge [N] sets the maximum precursor charge for the in silico library generation or library-free search--mbr-fix-settings when using the 'Unrelated runs' option in combination with MBR, the same settings will be used to process all runs during the second MBR pass--met-excision enables protein N-term methionine excision as variable modification for the in silico digest--min-cal [N] provide a guidance to DIA-NN suggesting the minimum number of IDs to use for mass calibration--min-class [N] provide a guidance to DIA-NN suggesting the minimum number of IDs to use for linear classifier training--min-corr [X] forces DIA-NN to only consider peak group candidates with correlation scores at least X--min-fr specifies the minimum number of fragments per precursors in the spectral library being saved--min-peak sets the minimum peak height to consider. Must be 0.01 or greater--min-pep-len [N] sets the minimum precursor length for the in silico library generation or library-free search--min-pr-charge [N] sets the minimum precursor charge for the in silico library generation or library-free search--min-pr-mz [N] sets the minimum precursor m/z for the in silico library generation or library-free search--missed-cleavages [N] sets the maximum number of missed cleavages--mod [name],[mass],[optional: 'label']

Course Project: NN Search on k-NN Graph and the Diversified k

E.g. some public data). If still not working, try to see if it's a mass calibration problem, by searching with a wide mass window first using --mass-acc-cal 100. Further, make sure that the predicted/empirical library used reflects the background proteome of your samples, not just specific proteins of interest.If DIA-NN has exited unexpectedly, could it be that it ran out of memory? Memory usage is expected to be high when (i) the search space is large, e.g. for phospho or metaproteomics searches, or when allowing lots of variable modifications - see the number of library precursors reported by DIA-NN: RAM usage to store the spectral library in memory is approximately 1Gb per 1 million of precursors. Try following the steps for reducing RAM usage outlined in Frequently asked questions (FAQ).Key publicationsPlease cite:DIA-NN: neural networks and interference correctionenable deep proteome coverage in high throughput Nature Methods, 2020Using DIA-NN for the analysis of post-translation modifications (PTMs), such as phosphorylation or ubiquitination: Time-resolved in vivo ubiquitinome profiling by DIA-MS reveals USP7 targets on a proteome-wide scale Nature Communications, 2021Using DIA-NN's ion mobility module for timsTOF data analysis or using DIA-NN in combination with FragPipe-generated spectral libraries: dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts Nature Communications, 2022Using DIA-NN for the analysis of multiplexed samples (SILAC, mTRAQ, etc): Increasing the throughput of sensitive proteomics by plexDIA Nature Biotechnology, 2022Using DIA-NN as part of the CysQuant workflow: CysQuant: Simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry Redox Biology, 2023Using DIA-NN's QuantUMS module for quantification: QuantUMS: uncertainty minimisation enables confident quantification in proteomics biorxivUsing DIA-NN to process Slice-PASEF data: Slice-PASEF: fragmenting all ions for maximum sensitivity in proteomics biorxivUsing DIA-NN as part of the MSFragger-DIA workflow in FragPipe: Analysis of DIA proteomics

Fast k-NN Search - arXiv.org

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. Fast NN search performance and has a fair comparison with hnswlib. no change to hnsw indexing and NN search. our NN search = hnsw layer0's NN search random seeds.

NNSearch: A Unified NN Search Framework - GitHub

Typical peptide from the LC system. Each MS1 spectrum represents the m/z values (mass over charge) and signal intensity values for the ions generated by the ion source ('precursor' ions), whereas each MS2 spectrum comprises m/z values for their fragments, generated in the collision cells of the mass spectrometer. Typically, to reduce the complexity of MS2 spectra, a mass filter (usually called 'Q1 quadrupole') is used to isolate a particular mass range of precursor ions for fragmentation, e.g. 500-520 m/z or 500.5-501.5 m/z - called 'mass isolation window' or 'selection window' (typically 2 m/z - 50 m/z in DIA).Spectral libraries. In order to quantify peptides and proteins from the raw data, DIA-NN needs to know which peptides to look for. For example, DIA-NN can be provided with a sequence database (e.g. a reference UniProt proteome in uncompressed .fasta format) as input. DIA-NN can then generate 'precursor ion queries' based on the sequence database. That is, DIA-NN in silico digests the database using the provided enzyme specificity (e.g. trypsin), applies fixed (always present) and variable (may or may not be present) modifications to the resulting peptides and generates 'precursors' as peptides at a particular charge state. Now, given this set of precursors, it's possible to generate all theoretical fragment ions (which are peptide N-terminal and C-terminal fragments produced by the breakage at the peptide bond) and search the raw data for occurances of those. However, raw data search turns out to be much more efficient if the theoretical properties of individual peptides/precursors are predicted with deep learning, i.e. the retention time (RT; term used to refer to elution time of the peptide from the liquid chromatography (LC) system), the ion mobility (IM) and the fragmentation pattern. DIA-NN can do this, with the result being an in silico predicted 'spectral library'. In

[1509. ] Fast k-NN search - arXiv.org

(K, diglycine), UniMod:888 (N-term, K, mTRAQ), UniMod:255 (N-term, K, dimethyl). Fine-tuning is likely to signficantly boost detection of other modifications.Tuning. To fine-tune DIA-NN's predictors, all you need is a spectral library (say, tune_lib.tsv, more on how to generate it yourself below) containing peptides bearing the modifications of interest. Type the following in Additional options and click Run:--tune-lib tune_lib.tsv--tune-rt--tune-imIf the library does not contain ion mobility information, omit --tune-im. If some modifications are not recognised, declare them using --mod. Tuning usually takes several minutes. DIA-NN will produce three output files: tune_lib.dict.txt, tune_lib.tuned_rt.pt and tune_lib.tuned_im.pt. You can now generated predicted libraries using tuned models by supplying the following options to DIA-NN:--tokens tune_lib.dict.txt--rt-model tune_lib.tuned_rt.pt--im-model tune_lib.tuned_im.ptGenerating the tuning library. If there is no suitable tuning library, one can always generate it directly from DIA data. For this, select one or several 'good' (typically, largest size) runs that are expected to contain peptides with the modifications of interest. These runs can also come from some public data set. Make a predicted library with DIA-NN by specifying all modifications of interest as variable or fixed (including those the predictor has already been trained on, if you expect to find them in the raw data). In vast majority of cases the max number of variable modifications can be set to 1-3, going higher is unlikely to be beneficial. Search the raw files using this predicted library in Proteoforms scoring mode, with Generate spectral library selected and MBR disabled. If the search space is large, the data comes from timsTOF, Orbitrap or Orbitrap Astral, and you would like to obtain the results quicker, set Speed and RAM usage to Ultra-fast. Optional: to optimise the performance, change the name of the output library and search the data again using --rt-window [X] and --im-window 0.2, where X is half of

Approximate k-NN search - OpenSearch Documentation

DIA-NN has been successfully used to search tens of thousands of runs lib-free, this is not really necessary in 99.9% of cases. In contrast, when processing large experiments, we recommend selecting 20 to 100 high quality runs (often selecting just the largest files works well) and create an empirical library from those (do not include blanks or failed runs here, they take by far the longest to process and are useless for library creation). This library can then be used to search the entire experiment (with MBR off). Further, if you have just acquired an experiment and want to quickly confirm that e.g. the runs did not fail and the mass calibration is OK, any suitable library can be used for this purpose, e.g. any public library or a DIA-based empirical library created based on a single run.Reducing the search space. The time to search a file with a large library is approximately proportional to the size of the library. Therefore, we recommend to strictly follow the recommendations in this guide with respect to specifying variable modifications (i.e. only specify them if there are compelling reasons for this), at least for the first analysis. Once you have the data obtained using recommended settings, can see if including extra modifications improves identification numbers (in vast majority of cases it does not).Reducing RAM usage. DIA-NN requires just under 0.5Gb RAM to store 1 million library precursors. That is a 3-million human tryptic digest library will require 1.5Gb RAM, while a 50-million library for phosphoproteomics will require about 25Gb of RAM. RAM is further used to store the raw data file that is being processed and for temporary storage of candidate PSMs. The requirements of the latter can be minimsed by adjusting Speed and RAM usage. There is currently a limit of max

Enhancing Search Capabilities with K-NN Vector

IntroductionThis is the 1st project of the course "Software Development for Algorithmic Problems". In thisproject, we achieved the following:implemented 2 differrent approaches to tackle the approximate nearest neighbour searchproblem: LSH and Hypercube Randomized Projectionimplemented the improved version of the well-known clustering algorithm k-Means, which iscalled k-Medians++The dataset used in the 2 tasks above was MNIST. Eachhandwritten digit image has a resolution of 28x28 pixels. Consequently, we store each imageas a "flattened" vector of size 784 (28 x 28 = 784). To calulate the distance between 2 pointsin our datasets we used the manhattan distanceNearest Neighbour SearchBoth methods mentioned above (LSH and Hypercube) work in a similar manner. The followingsequence of steps happens before the actual search process takes place:program reads in the input dataset (or training set), which in our case consists of 60.000images of handwritten digits (0 - 9).then, the program builds the actual data structures that will be used in the search process.All input dataset points are stored in these data structuresnext, the program reads in the query set (or test set)search starts: for each point in the query set find:its N nearest neighbours approximately (Approximate k-NN)its N nearest neighbours using brute-force search (Exact k-NN)its nearest neighbours approximately that lie inside a circle of radius RWhere these 2 methods differ, is how each one builds its appropriate data structures and chooses to store (hashing) the input dataset.In general, The whole purpose of these 2 methods is to deliver an efficient -but approximate-type search that significantly reduces search time compared to Exact k-NN, but it also produceshigh accuracy resultsClusteringUsing the same dataset file as input, the goal of this program is to "group" as accurately aspossible the input datapoints into clusters. Ideally, the clusters produced by the programshould only contain images of the same handwritten digit (default numbers of clusters is 10) .The (iterative) algorithm selects its initial centroids using an improved initializationtechnique called initialization++, assigns points to their closest centroid using one of Lloyd's or LSH orHypercube assignment methods and uses the median update rule to update the centroids.The algorithm stops, when the observed change in cluster assignments is relatively small. Forthis purpose, the k-medians objective function (l1 norm) is calculated after each iteration.ExecutionFor the LSH method, run the following commands: -L -o -N -R ">$ cd src/lsh $ make $ ./lsh -d ../../datasets/train-images-idx3-ubyte -q ../../datasets/t10k-images-idx3-ubyte -k -L -o -N -R For the Hypercube method, run the following commands: -M -probes -o -N -R ">$ cd src/cube $ make $ ./cube -d ../../datasets/train-images-idx3-ubyte -q ../../datasets/t10k-images-idx3-ubyte -k -M -probes -o -N -R Both methods can also run without explicit command line arguments i.e by simply running$ ./lsh or $ ./cube after of course navigating to the appropriate directory.In this. Fast NN search performance and has a fair comparison with hnswlib. no change to hnsw indexing and NN search. our NN search = hnsw layer0's NN search random seeds.

lazavgeridis/NN-search-and-Clustering-on-MNIST - GitHub

Generated using the Ultra-fast mode if you wish to obtain it quicker, we recommend trying this on Orbitrap, Orbitrap Astral and timsTOF data.RT window control. During the analysis, DIA-NN automatically sets the width of the retention time (RT) window: this value provides guidance to DIA-NN's algorithms that make decisions at which points in the acquisition to look for each particular precursor ion. Reducing RT window makes the search faster but increases the chances of DIA-NN failing to identify precursors that have inaccurate reference RT values stored in the spectral library. For a further speed increase - when generating an empirical library - one can use --rt-window-mul 1.7 --rt-window-factor 100: if there are no modification-associated biases in the input library retention times (i.e. it's either an empirical library or a predicted library with models tuned, if necessary, as recommended in Fine tuning prediction models), this will likely result in comparable identification numbers. Before doing this, we recommend verifying on several acquisitions that reduced RT window does not result in a noticeable loss of identification numbers with particular sample type and LC-MS settings.Optimising RAM access on Linux. To possibly increase performance on some systems, one can use mimalloc with dynamic override as described here for all steps except predicted library generation. However, in most cases this will have no effect. When running multiple DIA-NN instances in parallel, one per NUMA node, one may want to check if assigning a specific node to each instance with numactl (--cpunodebind and --preferred options, along with the --privileged Docker option when running as a Docker container) results in a speed improvement on the specific system.Incremental processingThis sections focuses on the ways to handle large experiments wherein raw data is being gradually added over a long period of time.Fast reanalysis. DIA-NN supports adding runs to the experiment

GitHub - lazavgeridis/NN-search-and-Clustering-on-MNIST

Declares a modification name. Examples: "--mod UniMod:5,43.005814", "--mod SILAC-Lys8,8.014199,label"--no-batch-mode disable batch mode, consequently, use all precursors for calibration--no-calibration disables mass calibration, not recommended--no-cut-after-mod [name] discard peptides generated via in silico cuts after residues bearing a particular modification--no-decoy-channel disables the use of a decoy channel for channel q-value calculation--no-fragmentation DIA-NN will not consider fragments other than included in the spectral library--no-fr-selection the selection of fragments for quantification based on the quality assessment of the respective extracted chromatograms will be disabled--no-isotopes do not extract chromatograms for heavy isotopologues--no-lib-filter the input library will be used 'as is' without discarding fragments that might be harmful for the analysis; use with caution--no-maxlfq disables MaxLFQ for protein quantification--no-ms1 do not consider MS1 data, use only during method optimisation to evaluate the impact of MS1 on the data quality--no-norm disables cross-run normalisation--no-peptidoforms disables automatic activation of peptidoform scoring when variable modifications are declared, not recommended--no-prot-inf disables protein inference (that is protein grouping) - protein groups from the spectral library will be used instead--no-prot-norm disable protein-level normalisation--no-quant-files instructs DIA-NN not to save .quant files to disk and store them in memory instead--no-rt-norm disable RT-dependent normalisation--no-rt-window disables RT-windowed search--no-skyline do not generate .skyline.speclib--no-stats disables the generation of the stats file--no-swissprot instruct DIA-NN not to give preference for SwissProt proteins when inferring protein groups--original-mods disables the automatic conversion of known modifications to the UniMod format names known to DIA-NN--out [file name] specifies the name of the main output report. The names of all other report files will be derived from this one--out-lib [file name] specifies the name of a spectral library to be generated--out-lib-copy copies the spectral library used into the output folder--peak-boundary [X] if the fragment or MS1 signal decays below its max / X, this is considered the boundary of the elution peak, affects among other algorithms the. Fast NN search performance and has a fair comparison with hnswlib. no change to hnsw indexing and NN search. our NN search = hnsw layer0's NN search random seeds.

Fast k-NN search - NASA/ADS

General, the term 'spectral library' refers to a set of known spectra, retention times and potentially also ion mobility values for selected precursor ions.Spectral libraries can differ based on how they are generated. What is described above is a predicted spectral library, which may contain millions of entries (e.g. a spectral library based on human UniProt proteome tryptic digest contains about 5 million precursors with charges 1 to 4). Further, spectral libraries can be empirically generated, i.e. contain only precursors observed in a particular experiment. A common strategy has been to perform offline fractionation of a peptide sample (e.g. whole-cell tryptic digest) with subsequent analysis of each of the fractions by LC-MS and the generation of a spectral library comprising the set of confidently identified precursors. This has been traditionally done with DDA, but also works with DIA. In fact, DIA-NN is capable of generating a library from the analysis of any DIA data. That is, one can take a predicted library, search some raw data with it and obtain as a result a much smaller empirical DIA-based library. This library can then be used for a quantitative analysis of the same DIA experiment but also other DIA experiments. The present guide contains detailed explanations of possible workflows based on DIA-NN and guidance on their use.FDR control. DIA data analysis produces a list of precursors and proteins identified in each of the samples of the experiment. Here 'identified' means that the software expects a particular proportion of those identifications, e.g. 1%, to be false, while the rest, e.g. 99%, are expected to be true. The way DIA-NN does this is by creating a list of likely PSMs (precursor-spectrum matches) and then narrowing it down to only retain PSMs passing certain quality thresholds. This kind of confidence in PSMs is represented