We give bounds on the distribution and fragmentation functions that appear at leading order in deep inelastic one-particle inclusive leptoproduction or in Drell-Yan processes. These bounds simply follow from positivity of the defining matrix elements and are an important guidance in estimating the magnitude of the azimuthal and spin asymmetries in these processes.PACS numbers: 13.85. Qk, In deep-inelastic processes the transition from hadrons to quarks and gluons is described in terms of distribution and fragmentation functions. For instance, at leading order in the inverse hard scale 1͞Q, the cross section for inclusive electroproduction e 2 H ! e 2 X is given as a charge squared weighted sum over quark distribution functions, which describe the probability of finding quarks in hadron H. In electron-positron annihilation, the one-particle inclusive cross section for e 1 e 2 ! hX is given as a charge squared weighted sum over quark and antiquark fragmentation functions, describing the decay of the produced (anti)quarks into hadron h.The distribution functions for a quark can be obtained from the light cone correlation function [1][2][3]
Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry‐based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label‐free kinase‐centric and substrate‐centric information in an Integrative Inferred Kinase Activity ( INKA ) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase–substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild‐type versus mutant, +/− drug), (iii) pre‐ and on‐treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient‐derived tumor xenografts with INKA ‐guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate‐based single‐sample tool KARP , and underscore target potential of high‐ranking kinases, encouraging further exploration of INKA 's functional and clinical value.
Summary We present an R package called iq to enable accurate protein quantification for label-free data-independent acquisition (DIA) mass spectrometry-based proteomics, a recently developed global approach with superior quantitative consistency. We implement the popular maximal peptide ratio extraction module of the MaxLFQ algorithm, so far only applicable to data-dependent acquisition mode using the software suite MaxQuant. Moreover, our implementation shows, for each protein separately, the validity of quantification over all samples. Hence, iq exports a state-of-the-art protein quantification algorithm to the emerging DIA mode in an open-source implementation. Availability and implementation The open-source R package is available on CRAN, https://github.com/tvpham/iq/releases and oncoproteomics.nl/iq. Supplementary information Supplementary data are available at Bioinformatics online.
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