dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , teams 15 and 17), typically under-performed with respect to specificity. Other teams achieved higher than average specificity scores at the cost of sensitivity (e. g. , teams 18 and 22), confirming the intuitive reciprocal relationship between these performance metrics. The individual search variables were then investigated through a series of controlled (in-house) searches using Byonic. For this purpose, the search settings were systematically varied from the 'default' search strategy used by most teams while keeping other parameters constant. Several search settings showed performance gains in terms of improved specificity (e. g. , literature-guided narrow glycan search space, SS1-SS2) or sensitivity (e. g. , decoy database disabled, SS13) but often at the expense of other performance characteristics (Fig.
4d ,e and Supplementary Table  19a ). While reduced sensitivity (glycoproteome coverage) may be an acceptable compromise for higher specificity (identification accuracy), the opposite arguably does not hold true. Thus, the considerable sensitivity gains and concomitant loss in specificity achieved by disabling the decoy database (SS13) did not benefit the data analysis. Instead, increasing the permitted glycans per peptide (SS9), tightening the allowed mass error (SS11-SS12) and relaxing the protease specificity benefitted both specificity and sensitivity. Search settings that showed cost-less performance gains were combined for subsequent rounds of iterative searches. Importantly, these efforts led to improved 'high accuracy' , 'high coverage' and 'balanced' (accuracy >< coverage) search strategies for N-and O-glycoproteomics (Fig.
Discussion This community study has objectively discerned the performance of current informatics solutions for glycoproteomics data analysis. Excitingly, several high-performance glycoproteomics software and search strategies were identified. Among the nine developer teams, Protein Prospector (team 2) was identified as the top performing software for both N-and O-glycoproteomics. Byonic (team 4) also displayed high performance for N-glycopeptide data analysis, and while this developer only demonstrated moderate performance for O-glycoproteomics, four Byonic user teams (teams 13, 15, 17 and 18) displayed the highest performance for O-glycopeptide data analysis. f, recommended byonic-centric search strategies for 'high accuracy', 'high coverage' and 'balanced' (between accuracy and coverage) glycoproteomics data analysis.
NATUrE METHODS Protein Prospector 51 and Byonic  33  , developed 10-20 years ago, have pioneered the glycopeptide informatics field and are search engines already commonly used in glycoproteomics  8, 31, 33. Protein Prospector is an academic (free) tool recognized for its ability to identify modified peptides and modification site(s) from LC-MS/MS data using a probability-difference-based scoring system  31. Protein Prospector is often a preferred search engine in studies addressing the challenging site annotation of O-glycopeptides, in particular when ET(hc)D-MS/MS data are available  5. However, Protein Prospector does not estimate the FDR of the glycan components of glycopeptides, which it regards as nondescript post-translation modifications with an exact mass, and the software may appear less user-friendly than competing tools.
Notably, GlycoPAT 37 (team 8) and glyXtool MS32 (team 3) were in our study also identified as high-performance N-and O-glycoproteomics software, respectively. Furthermore, IQ-GPA  30  (team 1) demonstrated merit for both N-and O-glycopeptide data analysis. While all three software packages handle high-resolution HCD-, ETciD-and EThcD-MS/MS data, IQ-GPA and GlycoPAT also identify glycopeptides based on high-resolution CID-MS/MS data and apply postsearch filtering based on advanced peptide and glycan decoy methods to estimate both peptide and glycan FDRs of glycopeptide candidates. The software glyXtool MS instead uses oxonium ions, Y 1 -ions (peptide-HexNAc), other glycopeptide-specific fragments and peptide-specific b-/y-ions to control FDR. These three academic tools were recently developed (<5 years ago) and, thus, hold a considerable potential in the field.
Most teams indeed returned data of the O-glycosylation site(s), but due to highly discrepant and often inconclusive reporting of sites and a paucity of literature on serum O-glycosylation sites, we were unable to score glycosylation site localization. Most software currently available for glycoproteomics data analysis participated in this study. However, several glycopeptide search engines; for example, pGlyco 52 , MSFragger-Glyco 53 , O-Pair Search 54 and StrucGP 55 , were unfortunately not represented due to LC-MS/MS data incompatibility or due to their development after the study period. Thus, this study is essentially a snapshot of the performance of software available at the time the data analysis was performed. Highlighting the rapid progress in glycoproteome informatics, most of the software solutions participating in this study have been improved and new versions released after the evaluation period.
For example, GPQuest v2. 0, GlycoPAT v1. 0 and Protein Prospector v5. 20. 23 tested herein have been superseded by more recent versions: namely, GPQuest v2. 1, GlycoPAT v2. 0 and Protein Prospector v. 6. 2. 2. Thus, a limitation of this study is that newer tools are available at the time of publication that were not compared in our analysis. Follow-up studies comparing the performance of these latest glycoproteomics software upgrades and informatics solutions not included in this study are therefore warranted. Beyond testing the ability of participants to identify the peptide and glycan components of glycopeptides from glycoproteomics data, such future comparative studies should ideally also test the ability to accurately quantify (relative, absolute) and report on modification sites of identified glycopeptides and could explore other relevant parameters not addressed herein including the use of alternative proteases, tandem mass tag-labeling and stepped-HCD-MS/ MS data among other experimental conditions gaining popularity in glycoproteomics.
Methods Study design and participants. Calls to join this study as a developer (academic/ commercial) or user of glycoproteomics software were made widely across the proteomics and glycomics community. In total, 25 teams signed up for the study, out of which 22 teams comprising nine developer and 13 user teams completed the study. All teams identified N-and O-glycopeptides from two communal glycoproteomics LC-MS/MS data files (Files A and B), and reported their findings using a common reporting template (PXD024101, PRIDE repository). While the user teams were guaranteed anonymity, the developers were informed that their software (hence, potentially their identity) would be disclosed on publication. The user teams were free to use any search engine(s) at their disposal including manual annotation/filtering of search output.
Developers returned the identified glycopeptides directly from their own software without manual postsearch filtering. The developers employed the following search engines: team 1: IQ-GPA v2. 5  30  ; team 2: Protein Prospector v5. 20. 23  31  ; team 3: glyXtool MS v0. 1. 4  32 ; team 4: Byonic v2. 16. 16  33  ; team 5: Sugar Qb 34 ; team 6: Glycopeptide Search v2. 0alpha  35  ; team 7: GlycopeptideGraphMS v1. 0/Byonic 36 ; team 8: GlycoPAT v2. 0 37 and team 9: GPQuest v2. 0 38 (see Supplementary Table  1  and below for overview of software and pre-and postprocessing tools used by all participants). The relative team performance was compared within (not between) the developer and user groups as these two groups were given slightly different instructions (above). Synthetic N-glycopeptide. An Asn building block carrying a disialylated, biantennary N-glycan (Hex 5 HexNAc 4 NeuAc 2 ) was purified from chicken egg yolk powder.
g. , phosphate, sulfate and acetylation) in the glycan search space. The participants were requested to report their team details, identification strategy and the identified glycopeptides in a common reporting template organized as five separate sheets in an Excel file comprising the following categories of information: (1) Team and contact details; (2) Identification strategy and other study information; (3) N-and O-glycan search space; (4) List of identified N-and O-glycopeptides and (5) Summary of identified glycopeptides and glycoproteins. The returned reports were carefully checked for compliance with the study guideline. See PXD024101 via the PRIDE repository  62  for the common reporting template and the deidentified reports from all participants forming the foundation of this study.
The adducted glycopeptides were combined with the corresponding nonadducted monosaccharide compositions. UniProtKB IDs were used as the source protein IDs. The glycopeptide IDs were written as the peptide sequence followed by the generic glycan composition. The comparisons between the generic glycan compositions, source proteins and glycopeptide IDs reported by the 22 teams were performed using the pivot table tool available in Excel, where the ID type was placed in 'rows' , and the team ID in 'columns'. The variables from each ID type were compared as summed counts across the 22 teams. Performance testing of teams and software. The relative team and software performance for glycopeptide data analysis was in this study determined via three different methods as detailed below. In short, all teams were first scored and ranked based on a comprehensive assessment method involving multiple complementary performance tests (1).
The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4. 0/. © The Author(s) 2021 50. Gizaw, S. T. , Gaunitz, S. & Novotny, M. V. Highly sensitive O-glycan profiling for human serum proteins reveals gender-dependent changes in colorectal cancer patients. Anal. Chem. 91, 6180-6189 (2019). 51. Chalkley, R. J. et al. Comprehensive analysis of a multidimensional liquid chromatography mass spectrometry dataset acquired on a quadrupole selecting, quadrupole collision cell, time-of-flight mass spectrometer: II.
Acknowledgements Rosa Viner  and  Sergei Snovida  ( Thermo Fisher Scientific ) are thanked for providing high-quality  LC-MS/MS data. Krishnatej Nishtala  is thanked for aiding the data analysis. Catherine Hayes ,  Julien Mariethoz  and  Frederique Lisacek  are thanked for informatics assistance. RK was supported by an  Early Career Fellowship  ( Cancer Institute   NSW ECF181259 ). DK was supported by an  Australian Research Council   Future Fellowship  ( FT160100344 ). GP was funded by  FAPESP  (n°  2018/15549-1 ). MT-A was supported by a  Macquarie University Safety Net  Grant. DK and NHP were supported by the  Australian Research Council Centre of Excellence in Nanoscale Biophotonics  ( CE140100003 ).
New developments in Protein Prospector allow for reliable and comprehensive automatic analysis of large datasets. Mol. Cell. Performance area Related test High-performance search settings a High-performance search Strategy may compromise output (expected) b efficient N-glycopeptide analysis (all-round performance) 1194-1204 (2005). 52. Liu, M. Q. et al. pGlyco 2. 0 enables precision N-glycoproteomics with Overall score (N1-N6) • Use decoy/contaminant protein database to establish peptide/protein FDr (SS13) Proteomics 4, comprehensive quality control and one-step mass spectrometry for intact • High m/z (SO2) • High monoisotopic correction (SO4) • High glycopeptide mass NA glycopeptide identification. Nat. Commun. 8, 438 (2017). (SO5) 53. Polasky, D. A. , Yu, F. , Teo, G. C. & Nesvizhskii, A.
I. Fast and comprehensive Accurate N-glycan N2 NA N-and O-glycoproteomics analysis with MSFragger-Glyco. Nat. Methods 17, 1125-1132 (2020). identification 54. Lu, L. , Riley, N. M. , Shortreed, M. R. , Bertozzi, C. R. & Smith, L. M. O-pair • High glycoPSm count (SO9) • High glycan mass (SO8) N-glycoproteome coverage Accurate source N-glycoprotein search with MetaMorpheus for O-glycopeptide characterization. Nat. Methods N3 NA 17, 1133-1138 (2020). • Late LC retention time (SO1) • High glycopeptide mass N-glycoproteome coverage identification (SO5) High N-glycoproteome N4 • Allow diversity of variable nonglycan • Late LC retention time (SO1) N-glycopeptide identification coverage peptide modifications (SS8) • High charge stage (SO3) accuracy, search time • Allow few glycans per peptide (SS9) • High glycopeptide mass • Allow multiple variable nonglycan (SO5) modifications per peptide (SS10) • High actual mass error (SO6) • Use decoy/contaminant protein • High glycan mass (SO8) database to establish peptide/protein • High glycoPSm count (SO9) FDr (SS13) reduced NeuGc and N6 • Allow few missed peptide cleavages • Low actual mass error (SO6) N-glycoproteome coverage multi-Fuc FDr (SS7) • Allow few variable nonglycan modifications per peptide (SS10) efficient O-glycopeptide Overall score • Use broad glycan database (SS2) • Large peptides (SO7) Search time analysis (all-round (O1-O5) • Allow few missed peptide cleavages • High glycoPSm count (SO9) performance) (SS7) • Allow multiple variable nonglycan modifications per peptide (SS10) Accurate O-glycan O1 • Use focused (narrow) glycan • High monoisotopic correction O-glycoproteome coverage identification database (SS2) (SO4) • Allow few missed peptide cleavages • Low actual mass error (SO6) (SS7) Accurate source O2 • Use broad glycan database (SS2) • High glycoPSm count (SO9) O-glycoproteome coverage, O-glycoprotein • Use full trypsin specificity (SS6) search time identification • Allow few missed cleavages (SS7) • Allow few glycans per peptide (SS9) • Allow multiple variable nonglycan modifications per peptide (SS10) High O-glycoproteome O3 • Use broad glycan database (SS2) • High actual mass error (SO6) O-glycopeptide identification coverage • High glycoPSm count (SO9) accuracy, search time Publisher's note.
NICER/XTI: The NICER X-ray observatory on board the International Space Station (ISS) has been carrying out full science operations since July 2017. XTI, which operates in the 0. 25-12 keV band, is the primary instrument on NICER and it consists of 56 co-aligned X-Ray Concentrators (XRC). Each XRC focuses X-rays into an aperture of a Focal Plane Module (FPM) which consists of a single pixel (non-imaging) Silicon Drift Detector (SDD;  (48) ). At the beginning of science operations, 52 of the 56 FPMs were operational and together they provide an effective area of roughly 1900 cm 2 at 1. 5 keV. This large effective area in the soft X-ray band combined with it's ability to provide an absolute time resolution of better than 300 nanoseconds makes NICER a unique facility to detect the fastest known soft X-ray astrophysical signals.
NICER started monitoring AT2018cow roughly five days after its discovery in the optical band on MJD 58285. 44 (6, 23) *. In total, 26 sets of observations were made between MJD 58290. 87 and 58349. 11 with observation IDs running between 1200250101 and 1200250126. These datasets were publicly available and we downloaded them from the HEASARC archive †. The cleaned events lists were extracted using the standard NICER Data Analysis Software (NICERDAS/HEASoft 6. 28) tasks nicercal, nimpumerge, and nicerclean. The latest NICER calibration release xti20200722 (22 July 2020) was used. The cleaned event files were barycenter-corrected using the barycorr ftools task. AT2018cow's optical coordinates (J2000. 0): (244. 000917, +22. 268031) were used along with refframe=ICRS and ephem=JPLEPH. 430. The Good Time Intervals (GTIs) were extracted with the nimaketime tool using the default filters: nicersaafilt=YES, saafilt=NO, trackfilt=YES, ang dist=0.
Neil Gehrels Swift/XRT: Neil Gehrels Swift started monitoring AT2018cow on MJD 58288. 44, roughly 3 days after its optical discovery. The observing cadence varied over the 58 d window coincident with the NICER observing campaign, i. e. , until MJD 58349. For the first ≈3 weeks, Neil Gehrels Swift observed AT2018cow 3-4 times per day and thereafter the cadence was reduced to roughly one exposure per day. The individual exposure duration varied between 200 and 2000 s. In this work we only used XRT data in the band pass of 0. 3-2. 5 keV to be consistent with NICER's energy range. We started our analysis with the publicly available, raw, level-1 data from HEASARC archive and reprocessed them with the xrtpipeline task of HEASoft. Initially, when the source was bright, data was taken in both the Windowed Timing (WT) and the Photon Counting (PC) modes.
XMM-Newton: XMM-Newton observed AT2018cow on three separate occasions roughly 37 (obsID: 0822580401; 33 ks), 82 (obsID: 0822580501; 45 ks), and 218 days (obsID: 0822580601; 56 ks) after its optical discovery. In this work, we only used the European Photon Imaging Camera (EPIC) data from the first and the last XMM-Newton epochs. The first exposure coincided with the NICER monitoring campaign while the last one was taken several months after AT2018cow ‡ https://www. swift. ac. uk/analysis/xrt/pileup. php faded away. As the data were taken in the full frame mode the Nyquist frequencies of the pn and MOS detectors were 6. 8 Hz and roughly 0. 2 Hz, respectively. To constrain the nature of variability on frequencies of a few Hz we only used pn data from the first dataset. For the last exposure MOS1 provides the best spatial resolution and hence we used only MOS1 data.
We started XMM-Newton/EPIC data reduction with the raw Observation Data Files (ODF) and reprocessed them using the XMM Science Analysis Software's (xmmsas version 17. 0. 0) tasks epproc and emproc for the pn and MOS data, respectively. We employed standard data filters of (PATTERN 12) and (PATTERN 4) for the pn and the MOS data, respectively. We only considered events in the soft X-ray band of 0. 25-2. 5 keV to be consistent with NICER's data (see section 1. 1). We removed intervals of background flaring by manually inspecting the 10-12 keV light curve as outlined in the XMM-Newton data analysis guide. The source count rates were extracted from a circular region of radius 33" which corresponds to 90% of the light from a point source as estimated by the fractional encircled energy of EPIC instruments.
NICER identifies a QPO candidate We first divided the NICER data into 256-s continuous segments and extracted their light curves with a time resolution of 1/2048 s. This resulted in a total of 105 light curve segments, i. e. , a cumulative exposure time of 26,880 s. With the mean count rate varying between 5. 7 and 0. 4 count s -1 this choice of 256 s ensured 100 counts in each segment. A Leahy normalized (Poisson noise level of 2) power density spectrum (PDS) was extracted from each of these individual light curves and they were all combined to obtain an average PDS (see the left panel of Fig. 1 ). The PDS is consistent with a value of 2 (Poisson noise level) at all frequencies except for excess around 224 Hz.
2. 2. 4 Confirming white noise at lower (mHz) frequencies using longer/continuous light curves from XMM-Newton To ensure that AT2018cow's soft X-ray PDS is consistent with white noise at lower frequencies, i. e. , 10 -3 -10 -2 Hz we used a ∼ 30 ks XMM-Newton exposure that coincided with NICER monitoring. NICER data are not ideal for sampling at these low frequencies because of short exposures. We extracted an average 0. 25-2. 5 keV XMM-Newton PDS to find that there is no evidence for red noise down to in the frequency range of ∼10 -3 -a few Hz. The EPIC-pn PDS with a Nyquist frequency of 6. 8 Hz is shown in Fig. S4. Also, if there is strong red noise at low frequencies (or long timescales) that can manifest as variability on faster timescales. This effect is known as red noise leakage  (52).
Ruling out an instrumental and a non-astrophysical origin All NICER events have two types of pulse height amplitude (PHA) data: the "slow" PHA derived from the slow chain electronics and the "fast" PHA (PHA FAST) derived by the fast chain electronics. The standard NICER calibration scripts convert this information into pulse invariant (PI) and PI FAST, respectively. For events with energies 600 eV the fast-chain is not triggered and thus PI FAST is undefined. Although NICER's XTI is a non-imaging instrument, there is a way to separate the background (particles, cosmic X-rays, and low-energy/optical light loading events) and the source events using the values of PI and PI FAST. The X-ray events from an on-axis astrophysical source, high-energy particles, light loading, and the cosmic X-ray background occupy a separate region of the PI vs PI RATIO (PI/PI FAST) plot §.
The signal is not present in the particle background High energy particles from space can interact with the silicon material in NICER/XTI's SDDs to produce charge and mimic X-ray events. These particle events are registered as enhancements in the so-called overshoot rate for each FPM. Also, because particles have energies much higher than NICER's nominal bandpass of 0. 25-12 keV their incidence is also evident in the 13-15 keV count rate. Thus, if this QPO signal were from background particle events it would also be present in the overshoot and the 13-15 keV data. We extracted the overshoot only event lists using the NICER data reduction pipeline described above but with a modified event flag EVENT FLAG=bxxx01x. This particular choice selects only the overshoot events. Then, similar to our analysis on source events, we applied barycenter correction on these unfiltered (but calibrated) events.
AT2018cow dominates the X-ray emission in NICER/XTI's FoV NICER/XTI is a non-imaging detector with a field of view of roughly 30 arcmin 2. Thus it is plausible that a contaminating point source other than AT2018cow could have produced this QPO signal. To investigate this possibility, we extracted an image by stacking the entire Neil Gehrels Swift/XRT archival data of AT2018cow (see the right panel of Fig. 3 ). It is clear from these images that AT2018cow is the brightest point source within XTI's FoV. The contribution from the other point sources is negligible. Furthermore, we can directly compare NICER/XTI light curve of AT2018cow's FoV with resolved XRT light curve. The variability features in XRT light curve are also evident in XTI data (see Fig. 2 ). This independently implies that AT2018cow dominates the X-ray emission in XTI's FoV.
A similar signal is NOT present in any AGN monitored by NICER during the same epoch AGN host supermassive black holes ( 10 5 M ) and hence causality argument suggests that they should not show any variability on timescales of milliseconds, i. e. , hundreds of Hz. Therefore, to be absolutely sure that the 224 Hz QPO signal is unique to AT2018cow data we also extracted average PDS of 3 active galactic nuclei that NICER monitored during the same epoch, i. e. , only using their observations between MJD 58290. 87 and 58349. 11. These resulting average PDS are shown in Fig. S10  and a 224 Hz feature is not seen in any of them.