Mass spec predictor

Author: p | 2025-04-25

The program will compare the observed mass spec peak to the theoretical mass spec peaks of multiple mass spec adducts. The program is capable of calculating H, Na, and K adducts with

Predictors of thrombotic complications and mass effect

During mutation during decoy generation to be at least X in absolute value, default 15.0--dg-max-mut [X] aim for the precursor mass shift during mutation during decoy generation not to exceed X in absolute value, default 50.0--dir [folder] specifies a folder containing raw files to be processed. All files in the folder must be in .raw, .wiff, .mzML or .dia format--dir-all [folder] as --dir, but recursive over subfolders--direct-quant disable QuantUMS and use legacy DIA-NN quantification algorithms instead, also disables channel-specific protein quantification when analysing multiplexed samples--dl-no-fr when using the deep learning predictor, prediction of fragment intensities will not be performed--dl-no-im when using the deep learning predictor, prediction of ion mobilities will not be performed--dl-no-rt when using the deep learning predictor, prediction of retention times will not be performed--duplicate-proteins instructs DIA-NN not to skip entries in the sequence database with duplicate IDs (while by default if several entries have the same protein ID, all but the first entry will be skipped)--export-quant add fragment quantities, fragment IDs and quality information to the .parquet output report--ext [string] adds a string to the end of each file name (specified with --f)--f [file name] specifies a run to be analysed, use multiple --f commands to specify multiple runs--fasta [file name] specifies a sequence database in FASTA format (full support for UniProt proteomes), use multiple --fasta commands to specify multiple databases--fasta-filter [file name] only consider peptides matching the stripped sequences specified in the text file provided (one sequence per line), when processing a sequence database--fasta-search instructs DIA-NN to perform an in silico digest of the sequence database--fixed-mod [name],[mass],[sites],[optional: 'label'] - adds the modification name to the list of recognised names and specifies the modification as fixed. Same syntax as for --var-mod. Has an effect of (i) applying fixed modifications during FASTA digest or (ii) declaring fixed modifications

Mass Spec Data and the Mass Spectrometrist - ACD/Labs

Deep learning predictor--top [N] set N for the top N protein quantification, default N = 1--tune-im tune the IM deep learning predictor, requires --tune-lib--tune-lib [file] specifies the library to be used for deep learning predictor fine-tuning, may require declaring unknown modifications with --mod--tune-lr [X] specifies fine-tuning learning rate, default is 0.00001--tune-restrict-layers keep RNN layer weights fixed when fine-tuning--tune-rt tune the RT deep learning predictor, requires --tune-lib--use-quant use existing .quant files, if available--verbose [N] sets the level of detail of the log. Reasonable values are in the range 0 - 4--var-mod [name],[mass],[sites],[optional: 'label'] - adds the modification name to the list of recognised names and specifies the modification as variable. [sites] can contain a list of amino acids and 'n' which codes for the N-terminus of the peptide. '*n' indicates protein N-terminus. Examples: "--var-mod UniMod:21,79.966331,STY" - phosphorylation, "--var-mod UniMod:1,42.010565,*n" - N-terminal protein acetylation. Similar to --mod can be followed by 'label'. Has an effect of (i) applying variable modifications during FASTA digest or (ii) declaring modifications to be localised during raw data analysis--var-mods sets the maximum number of variable modifications--xic [optional: X] instructs DIA-NN to extract MS1/fragment chromatograms for identified precursors within X seconds from the elution apex, with X set to 10s if not provided; the chromatograms are saved in .parquet files (one per run) located in a folder that is created in the same location as the main output report; equivalent to the 'XICs' option in the GUI--xic-theoretical-fr makes DIA-NN extract chromatograms for all theoretical charge 1-2 fragment ions, including those with common neutral losses, if --xic is used--window [N] sets the scan window radius to a specific value. Ideally, should be approximately equal to the average number of MS/MS data points per peakMain output reference Description of selected columns in the main .parquet reportRun raw file name without

HDExaminer – Sierra Analytics - Mass Spec

Calculators Pace Calculator Distance Converter VO2 Max Estimator Race Time Predictor BMI Calculator Calories Estimator Body mass index (BMI) is an approximation of an average adult's body fat by calculating a ratio between your weight and your height. While it is a good estimation for most people, it is rather inaccurate for people with low body fat or who are muscular. You should take this value as a suggestion and not as the absolute truth. Usage: Enter your height and weight and your BMI will be automatically calculated. Unit: Metric Height: Weight: BMI: N/A Interpreting your BMI* Category BMI range Underweight Below 18.5 Normal 18.5 - 24.9 Overweight 25.0 - 29.9 Obese 30.0 - 39.9 Extreme obesity 40 or above * This information is taken from the National Institute of Health (NIH). For more information regarding your weight and how to lead a healthy life style, please refer to NIH's Aim For A Healthy Weight.. The program will compare the observed mass spec peak to the theoretical mass spec peaks of multiple mass spec adducts. The program is capable of calculating H, Na, and K adducts with

Mass Spec Fragment Prediction Software

The results showed a positive correlation between Log LM/VFM and BMD, with an increase in BMD as LogLM/VFM increased. In regression analysis, to minimize potential bias, we extensively considered possible confounding factors to ensure the reliability of the results. At the same time, we further conducted a stratified analysis based on gender, age, BMI, hypertension, and diabetes. Except for the diabetic population, the subgroup analysis of all populations showed statistical significance, and it should be noted that this may be related to the small sample size of the diabetic population. These results all indicate that the conclusion has a considerable degree of robustness. In further analysis of smooth curve fitting and threshold effects, we found a nonlinear trend and threshold effect between LogLM/VFM and BMD.Lean body mass refers to the body’s total weight, excluding adipose tissue or fat. One of the critical components of lean mass is skeletal muscle, which is responsible for movement and stability. Despite being a passive structural element, skeletal muscles function as an endocrine organ that releases an extensive range of muscle factors known as myokines, such as insulin-like growth factor-1, fibroblast growth factor-2, brain-derived neurotrophic factor et al., which play a role in regulating other cells in the body [27]. These muscle factors actively participate in bone metabolism by interacting with the osteoblasts or osteoclasts. Previous research has established a positive correlation between lean mass and BMD. Ilesanmi-Oyelere et al. demonstrated that BMD was more strongly associated with lean body mass than fat mass [28]. Likewise, Xiao’s study revealed that appendicular lean mass was a robust predictor of BMD in both genders [29]. Our study excluded bone mineral content from assessing lean body mass to avoid potential interference. The results showed a positive association between lean body mass and BMD, and this relationship did not

PNNL-Comp-Mass-Spec/Package_Folder_Create_Manager

Calculation of start/stop RT values reported in the main .parquet report--peak-translation instructs DIA-NN to take advantage of the co-elution of isotopologues, when identifying and quantifying precursors; automatically activated when using --channels--peptidoforms enables peptidoform confidence scoring--pg-level [N] controls the protein inference mode, with 0 - isoforms, 1 - protein names (as in UniProt), 2 - genes--predict-n-frag [N] specifies the maximum number of fragments predicted by the deep learning predictor, default value is 12--predictor instructs DIA-NN to perform deep learning-based prediction of spectra, retention times and ion mobility values--prefix [string] adds a string at the beginning of each file name (specified with --f) - convenient when working with automatic scripts for the generation of config files--prosit export prosit input based on the FASTA digest--proteoforms enables the proteoform confidence scoring mode--pr-filter [file name] specify a file containing a list of precursors (same format as the Precursor.Id column in DIA-NN output), FASTA digest will be filtered to only include these precursors--qvalue [X] specifies the precursor-level q-value filtering threshold--quant-acc [X] sets the precision-accuracy balance for QuantUMS to X, where X must be between 0 and 1--quant-ori-names .quant files will retain original raw file names even if saved to a separate directory, convenient for .quant file manipulation--quant-fr [N] sets the number of top fragment ions among which the fragments that will be used for quantification are chosen for the legacy (pre-QuantUMS) quantification mode. Default value is 6--quick-mass-acc (experimental) when choosing the MS2 mass accuracy setting automatically, DIA-NN will use a fast heuristical algorithm instead of IDs number optimisation--quant-no-ms1 instructs QuantUMS not to use the recorded MS1 quantities directly--quant-params [params] use previously obtained QuantUMS parameters--quant-sel-runs [N] instructs QuantUMS to train its parameters on N automatically chosen runs, to speed up training for large experiments, N here must be 6 or greater--quant-tims-sum for slice/scanning timsTOF methods, calculate intensities

Mass Spec Simulator - Prot pi

Sign in to your ScreenRant account Across the entire Mass Effect trilogy, players will have the opportunity to select a class for their Commander Shepard from the six available options. Each class allows Shepard to specialize in Biotics, Combat, Tech, or some combination of two of these. By far one of the most popular options is Vanguard, which combines weapons and combat specializations with Biotics to create the ultimate Biotic Warrior. Vanguards across the Mass Effect trilogy are deadliest at close range and engage in high-risk, high-reward combat that often includes charging an enemy and detonating a biotic power or blasting them with a shotgun. This can be dangerous, making Vanguard a tricky class to master. There are a few differences in a Vanguard's abilities between the three games in the Mass Effect trilogy. In the original series, they are the only class for which all weapons are available. In Mass Effect: Legendary Edition, Vanguards will still have this access, but so will all classes. When building a Vanguard Shepard, players will want to focus on increasing both weapon and power damage output, improving shields, and selecting an appropriate Bonus Power. Here's how to build the Vanguard class in Mass Effect. How to Spec Vanguard Abilities in Mass Effect: Legendary Edition Vanguards across the Mass Effect trilogy will have access to most of the same abilities, with a few minor differences. In Mass Effect 2 and Mass Effect 3, after reaching Level 4 on an ability, players can select powerful. The program will compare the observed mass spec peak to the theoretical mass spec peaks of multiple mass spec adducts. The program is capable of calculating H, Na, and K adducts with