msqrob2.bib

% 32321741
@Article{sticker2020,
   Author="Sticker, A.  and Goeminne, L.  and Martens, L.  and Clement, L. ",
   Title="{{R}obust {S}ummarization and {I}nference in {P}roteome-wide {L}abel-free {Q}uantification}",
   Journal="Mol Cell Proteomics",
   Year="2020",
   Volume="19",
   Number="7",
   Pages="1209--1219",
   Month="07"
}

% 32227882
@Article{goeminne2020,
   Author="Goeminne, L. J. E.  and Sticker, A.  and Martens, L.  and Gevaert, K.  and Clement, L. ",
   Title="{{M}{S}q{R}ob {T}akes the {M}issing {H}urdle: {U}niting {I}ntensity- and {C}ount-{B}ased {P}roteomics}",
   Journal="Anal Chem",
   Year="2020",
   Volume="92",
   Number="9",
   Pages="6278--6287",
   Month="05"
}

% 26566788
@Article{goeminne2016,
   Author="Goeminne, L. J.  and Gevaert, K.  and Clement, L. ",
   Title="{{P}eptide-level {R}obust {R}idge {R}egression {I}mproves {E}stimation, {S}ensitivity, and {S}pecificity in {D}ata-dependent {Q}uantitative {L}abel-free {S}hotgun {P}roteomics}",
   Journal="Mol Cell Proteomics",
   Year="2016",
   Volume="15",
   Number="2",
   Pages="657--668",
   Month="Feb"
}

@ARTICLE{Huang2020-lu,
  title    = "{MSstatsTMT}: Statistical Detection of Differentially Abundant
              Proteins in Experiments with Isobaric Labeling and Multiple
              Mixtures",
  author   = "Huang, Ting and Choi, Meena and Tzouros, Manuel and Golling,
              Sabrina and Pandya, Nikhil Janak and Banfai, Balazs and Dunkley,
              Tom and Vitek, Olga",
  journal  = "Mol. Cell. Proteomics",
  volume   =  19,
  number   =  10,
  pages    = "1706--1723",
  abstract = "Tandem mass tag (TMT) is a multiplexing technology widely-used in
              proteomic research. It enables relative quantification of proteins
              from multiple biological samples in a single MS run with high
              efficiency and high throughput. However, experiments often require
              more biological replicates or conditions than can be accommodated
              by a single run, and involve multiple TMT mixtures and multiple
              runs. Such larger-scale experiments combine sources of biological
              and technical variation in patterns that are complex, unique to
              TMT-based workflows, and challenging for the downstream
              statistical analysis. These patterns cannot be adequately
              characterized by statistical methods designed for other
              technologies, such as label-free proteomics or transcriptomics.
              This manuscript proposes a general statistical approach for
              relative protein quantification in MS- based experiments with TMT
              labeling. It is applicable to experiments with multiple
              conditions, multiple biological replicate runs and multiple
              technical replicate runs, and unbalanced designs. It is based on a
              flexible family of linear mixed-effects models that handle complex
              patterns of technical artifacts and missing values. The approach
              is implemented in MSstatsTMT, a freely available open-source
              R/Bioconductor package compatible with data processing tools such
              as Proteome Discoverer, MaxQuant, OpenMS, and SpectroMine.
              Evaluation on a controlled mixture, simulated datasets, and three
              biological investigations with diverse designs demonstrated that
              MSstatsTMT balanced the sensitivity and the specificity of
              detecting differentially abundant proteins, in large-scale
              experiments with multiple biological mixtures.",
  month    =  oct,
  year     =  2020,
  keywords = "Mass spectrometry; TMT; bioinformatics software; hypothesis
              testing; mathematical modeling; multiple mixtures; protein
              quantification; quantification; statistics",
  language = "en"
}

@ARTICLE{Plubell2017-th,
  title     = "Extended multiplexing of tandem mass tags ({TMT}) labeling
               reveals age and high fat diet specific proteome changes in mouse
               epididymal adipose tissue",
  author    = "Plubell, Deanna L and Wilmarth, Phillip A and Zhao, Yuqi and
               Fenton, Alexandra M and Minnier, Jessica and Reddy, Ashok P and
               Klimek, John and Yang, Xia and David, Larry L and Pamir, Nathalie",
  journal   = "Mol. Cell. Proteomics",
  publisher = "Mol Cell Proteomics",
  volume    =  16,
  number    =  5,
  pages     = "873--890",
  abstract  = "The lack of high-throughput methods to analyze the adipose tissue
               protein composition limits our understanding of the protein
               networks responsible for age and diet related metabolic response.
               We have developed an approach using multiple-dimension liquid
               chromatography tandem mass spectrometry and extended multiplexing
               (24 biological samples) with tandem mass tags (TMT) labeling to
               analyze proteomes of epididymal adipose tissues isolated from
               mice fed either low or high fat diet for a short or a long-term,
               and from mice that aged on low versus high fat diets. The
               peripheral metabolic health (as measured by body weight,
               adiposity, plasma fasting glucose, insulin, triglycerides, total
               cholesterol levels, and glucose and insulin tolerance tests)
               deteriorated with diet and advancing age, with long-term high fat
               diet exposure being the worst. In response to short-term high fat
               diet, 43 proteins representing lipid metabolism (e.g. AACS,
               ACOX1, ACLY) and red-ox pathways (e.g. CPD2, CYP2E, SOD3) were
               significantly altered (FDR < 10\%). Long-term high fat diet
               significantly altered 55 proteins associated with immune response
               (e.g. IGTB2, IFIT3, LGALS1) and rennin angiotensin system (e.g.
               ENPEP, CMA1, CPA3, ANPEP). Age-related changes on low fat diet
               significantly altered only 18 proteins representing mainly urea
               cycle (e.g. OTC, ARG1, CPS1), and amino acid biosynthesis (e.g.
               GMT, AKR1C6). Surprisingly, high fat diet driven age-related
               changes culminated with alterations in 155 proteins involving
               primarily the urea cycle (e.g. ARG1, CPS1), immune
               response/complement activation (e.g. C3, C4b, C8, C9, CFB, CFH,
               FGA), extracellular remodeling (e.g. EFEMP1, FBN1, FBN2, LTBP4,
               FERMT2, ECM1, EMILIN2, ITIH3) and apoptosis (e.g. YAP1, HIP1,
               NDRG1, PRKCD, MUL1) pathways. Using our adipose tissue tailored
               approach we have identified both age-related and high fat diet
               specific proteomic signatures highlighting a pronounced
               involvement of arginine metabolism in response to advancing age,
               and branched chain amino acid metabolism in early response to
               high fat feeding. Data are available via ProteomeXchange with
               identifier PXD005953.",
  month     =  may,
  year      =  2017,
  language  = "en"
}