dataesr/scientific-paragraphs-categorization huggingface dataset:
In terms of specificity, the addition of 2Kb flanking sequences increases significantly the quality of all the programs (Augustus: p-value = 2. 87 × 10 -7 , Genscan: pvalue = 1. 27 × 10 -9 , GeneID: p-value = 8. 46 × 10 -5 , Glim-merHMM: p-value = 2. 78 × 10 -7 , Snap: p-value = 1. 03 × 10 -17 ). This is probably due to the addition of specific signals in the genomic environment of the gene (further than 150 bp from the gene boundaries), such as the promoter, enhancers/silencers, etc. that are taken into account in the program prediction models. At the exon level, the effect of the flanking sequences is not the same for the different programs. For example, the sensitivity of Augustus (p-value = 4. 06 × 10 -4 ), Genscan (p-value = 1. 59 × 10 -8 ) and GeneID (p-value = 2. 98 × 10 -2 ) is highest when the input sequence has 150 bp flanking regions and significantly decreases when 2Kb flanking nucleotides are added, while for GlimmerHMM (p-value = 0.
We then assessed the effect of exon lengths on the prediction quality of the five programs, using the 756 Confirmed sequences without UDT regions. Figure  10a  and Additional file 1: Table  S10A  show the proportion of Correct exons (both 5′ and 3′ exon boundaries correctly predicted) depending on the exon length. The short exons (< 50 nucleotides) are generally the least accurate, with the best program, Augustus, achieving only 18% Correct short exons. Medium length exons (50-200 nucleotides) are predicted better than longer exons (> 200 nucleotides) for Augustus and Genscan. To further investigate the exon prediction, each exon predicted by a gene prediction program was classified as 'Correct' if both exon boundaries were correctly predicted, 'Wrong (5')' or 'Wrong (3′)' if the 5′ or 3′ exon boundary was badly predicted respectively, and 'Wrong' if both boundaries were badly predicted.
For proteins with < 100 amino acids, GlimmerHMM achieves the best results with 68% sequence identity and five (25%) perfectly predicted proteins (100% identity), while Augustus obtains only 57% sequence identity and four perfectly predicted proteins. We then studied the phylogenetic origin of the proteins and the availability of suitable species models in the different programs. Figure  12  and Additional file 1: Table  S12  show the performance of the five gene prediction programs for the sequences in the different clades in G3PO. The accuracy of each program is highly variable between the different clades, probably due to the availability of suitable prediction models for some species. For the sequences in the Craniata clade, Augustus and Genscan achieve the highest accuracy (72 and 70% respectively), while Snap has the lowest accuracy (33%).
Effect of protein sequence errors Finally, we investigated the performance of the prediction programs for the 904 Unconfirmed sequences, where potential sequence errors were observed in the benchmark sequences. As mentioned above, the G3PO benchmark sequences were extracted from the Uniprot database, which means that many of the proteins are not supported by experimental evidence. In this test, we wanted to estimate the prediction accuracy of the five gene prediction programs for the Unconfirmed benchmark sequences. Since the Unconfirmed sequences could not be used as a ground truth, here we measured prediction accuracy based on a closely related Confirmed sequence (see Methods). Table  2  shows the prediction accuracies achieved by each program for the sets of Confirmed and Unconfirmed sequences.
Discussion Several recent reviews  [3, 22, 23]  have highlighted the fact that automated genome annotation strategies still have difficulty correctly identifying protein-coding genes. This failure might be explained by the quality of the draft genome assemblies, the complexity of eukaryotic exon maps, high levels of genetic sequence divergence or deviations from canonical genetic characteristics  [36]. Consequently, it is essential to benchmark the existing different gene prediction strategies to assess their reliability, to identify the most promising approaches, but also to limit the spread of errors in protein databases  [37]. An ideal benchmark for gene prediction programs should include proteins encoded by real genomic sequences. Unfortunately, most of the protein sequences in the public databases have not been verified by experimental means, with the exception of the manually annotated Swiss-Prot sequences (representing only 0.
This in-depth characterization allowed us to investigate the impact of these features on gene prediction accuracy. One of the main limitations of the benchmark concerns the fact that the protein sequences were extracted from the Uniprot database, where a 'canonical' protein isoform is defined based on cross-species conservation and the conservation of protein structure and function. Consequently, programs that predicted more minor isoforms created by alternative splicing events were penalized in our evaluations. Unfortunately, there is currently no ideal solution to this. In the future, gene prediction programs will need to evolve to predict all isoforms for a gene. Another limitation of the benchmark concerns the evaluation of the gene prediction results with respect to a single benchmark sequence.
13. The in-depth characterization of the benchmark sequences and the detailed information extracted from the analyses provide essential elements that could be used to improve model training and therefore gene prediction. It may be interesting to further analyze the weaknesses identified, including small proteins, very long proteins, proteins coded by a large number of exons, proteins from nonmodel organisms, etc. Finally, the Unconfirmed sequences identified in this study represent a goldmine for the identification of atypical gene features, for example atypical regulatory signals or splice sites, that are not fully taken into account in the current prediction models. More than 50% of the original reference protein sequences extracted from public databases were found to contain at least one error.
Benchmark test sets To construct a benchmark set of eukaryotic genes, we selected the 20 human Bardet-Biedl Syndrome (BBS) proteins (Additional file 1: Table  S2 ). Based on this initial gene set, we extended the test sets using the pipeline shown in Fig. 14  and described in detail below. (i) For each of the 20 human proteins, orthologous proteins were identified in 147 eukaryotic organisms (Additional file 1: Table  S1 ) using OrthoInspector version 3. 0  [46] , which was built using proteins from the Uniprot Reference Proteomes database  [34]  (Release 2016_11). For each species, we selected one ortholog sharing the highest percent identity with the human sequence. This resulted in a total of 1793 protein sequences, of which 65 (3. 6%) were found in the curated Swissprot database. The number of proteins in each BBS family is provided in Additional file 1: Table  S2   S3 ).
SIBIS classifies the potential sequence errors into 9 categories: Nterminal deletion, N-terminal extension, N-terminal mismatched segment, C-terminal deletion, Cterminal extension, C-terminal mismatched segment, internal deletion, internal insertion and internal mismatched segment. Of the 1793 protein sequences identified in step (i), 889 proteins had no errors (called "Confirmed") and 904 proteins had at least one potential error (called "Unconfirmed"). At this stage, the BBS14 protein was excluded from the benchmark because the MSA contained too many misalignments. (iii)For each orthologous protein, the genomic sequence was extracted from the Ensembl database  [35]. Genomic sequences were extracted with the 'soft mask' option, i. e. repeated or low complexity regions are replaced by lower case nucleotides.
These are generally ignored by gene prediction programs. We also found regions with 'n' characters, which are used to indicate undetermined or ambiguous nucleotides (IUPAC nomenclature) probably caused by genome sequencing errors or assembly gaps. A sequence segment with a run of n characters was defined as an undetermined (UDT) region. Additional file 1: Table  S5  summarizes the general statistics of these 283 sequences with UDT regions. Finally, we identified the Ensembl transcript corresponding to the Uniprot protein sequence, (generally the 'canonical transcript' from APPRIS  [48] ) in order to construct the exon map by extracting the positions of all exons/introns, including the 5′/3′ untranslated regions when available. (iv) For the baseline tests, we included flanking sequences of length 150 bases upstream and downstream of the gene.
Gene prediction methods The programs tested are listed in All programs were run on an Intel(R) Xeon(R) CPU E5-2695 v2 @ 2. 40Ghz, 12 cores, with 256 Go RAM. Each prediction program was run with the default settings, except for the species model to be used. As the benchmark contains sequences from a wide range of species, we selected the most pertinent training model for each target species, based on the taxonomic proximity between the target and model species. For each program, we compared the taxonomy of the target species with the taxonomy for each model species available, where taxonomies were obtained from the NCBI Taxonomy database (https://www. ncbi. nlm. nih. gov/taxonomy). We then selected the model species that was closest to the target in the taxonomic tree.
WEScore ¼ WE Total number of predicted exons We also determined the proportion of correctly predicted 5′ and 3′ exon boundaries, as follows: At the protein level, we measure the accuracy of the protein products predicted by a program. Since a program may predict more than one transcript for a given gene sequence in the benchmark, we calculate the percent identity between the benchmark protein and all predicted proteins and the predicted protein with the highest percent identity score is selected. To calculate the percent identity score between the benchmark protein and the predicted protein, we construct a pairwise alignment using the MAFFT software (version 7. 307)  [51]  and the percent identity is then defined as: %Identity ¼ Number of identical amino acidsÃ100 Length of benchmark protein.
Evaluation metric for unconfirmed benchmark proteins Since the Unconfirmed proteins in the benchmark are badly predicted and have at least one identified sequence error, the %Identity score defined above for the Confirmed sequences cannot be used. Instead, we compare the protein sequences predicted by the programs with the most closely related Confirmed sequence found in the corresponding MSA. Thus, for a given Unconfirmed sequence, E, we calculated the sequence identity between E (excluding the sequence segments with predicted errors) and all the orthologous sequences in the corresponding MSA. If a Confirmed orthologous sequence, V, was found that shared ≥50% identity with E, then the sequence V was used as the reference protein to evaluate the program prediction accuracy. As before, a pairwise alignment between the prediction protein and sequence V was constructed using MAFFT and the %Identity score was calculated.
(2011)  and updated to include far-IR and sub-mm continuum fluxes from the Herschel Hi-GAL survey  (Molinari et al. 2016). The 1. 4 mm continuum emission also shows part of a filament that extends much further 1 Latest value taken from the RMS database: http://rms. leeds. ac. uk/cgi-bin/public/RMS_DATABASE. cgi A118, page 3 of 44 A&A 636, A118 (2020).
The same flux calibrator (MWC349), and the same phase and amplitude calibrator (0059+581), were used for all tracks. Calibration was carried out using the CLIC module within GILDAS 2. For line data, the continuum was subtracted using linefree channels in the same band, while a combined continuum Notes. ( a) Bands 01-08 used the Narrow-Band, high spectral resolution correlator while bands 09-12 used the WideX low spectral-resolution correlator. ( b) As in bands 01-04. ( c) As in bands 05-08. dataset was formed from all WideX band channels which are completely free of line emission at all positions. The beam-size of the continuum with uniform weighting (which results in the highest resolution) is 0. 45 × 0. 32 , increasing to 0. 94 × 0. 82 with natural weighting. The identification of detected transitions towards W3 IRS4 is discussed in Sect.
5 by 1. 5 centred on the source, with full-width at half-maximum (FWHM) beam-sizes of 11. 3 at 217 GHz and 10. 6 at 233 GHz. In order to reduce potential scanning artefacts, a basket-weave observing strategy was employed such that one complete dataset includes a pair of maps, one with the major scanning direction in right ascension and one in declination. The spatial sampling was better than Nyquist, approximately every 4 , with each position observed in both horizontal (H) and vertical (V) polarisation. The reference positions for each source were checked prior to starting the mapping observations to ensure that they were free from emission. The data were reduced and processed using both standard and custom routines with the CLASS module within GILDAS. Details are given in Appendix A. 1. The final maps have a typical noise of ∼0.
Two imaging techniques are used in this paper, Clark  (Clark 1980 ) and  Steer-Dewdney-Ito (SDI; Steer et al. 1984) , depending on whether the distribution of emission is point-like or extended respectively. Transitions in the highand low-resolution bands (see Table  4 ) are imaged with a velocity resolution of 0. 5 and 3. 0 km s -1 respectively. The weighting given to visibilities on different baselines for a given dataset is chosen to balance spatial resolution against sensitivity (for example uniform or natural weighting, see Fig. 2 ). More details on the merging and imaging are given in Appendix B. For W3 IRS4 the merged data are used with the SDI cleaning method for 13 CO 2-1, C 18 O 2-1, SO 6 5 -5 4 , H 2 CO 3 0,3 -2 0,2 , H 2 CO 3 2,2 -2 2,1 , HC 3 N 24-23, and CH 3 OH 4 2,2 -3 1,2 , which all show extended emission, while the NOEMA only data and Clark method are used for OCS 18-17, HNCO 10 1,9 -9 1,8 , and CH 3 CN 12-11 K = 0-6, which only show compact emission.
Comparing to Fig. 6  of  Hoare & Franco (2007) , which compares L bol and L radio for known HCH II and UCH II regions, as well as wind and jet sources, W3 C lies within the group of H II regions while IRS4 clearly lies within the group of wind and/or jet sources. Thus it is much more likely that the hot core W3 IRS4 is a radio-quiet MYSO with the cm emission originating from a radio jet, rather than a source which powers an H II region. We discuss the implications this has for the central source of IRS4 in Sect. 5. With the radio emission indicating the presence of an ionised radio jet or disc-wind, it is obvious to next try to constrain the direction and properties of the outflow from IRS4. However, neither the IRAM 30 m, NOEMA nor merged data show any noticeable sign of high-velocity line-wing emission, with similar line-shapes in the 30 m spectra between 12 CO, 13 CO and C 18 O (see for example Fig.
an outflow. However, rotation as a function of radius can also be seen where a rotating disc is embedded in an infalling envelope, as shown by  van 't Hoff et al. (2018) , for example, in their modelling of ALMA data for the Class 0 low-mass YSO L1527 (see particularly their Figs. 5  and D. 1). This shift is quite abrupt in their models, close to the outer radius of the disc, which is again reminiscent of the OCS and CH 3 CN data for W3 IRS4. The rotation in the direction of the velocity gradient in the moment 1 maps of the denser and higher-temperature tracing lines suggests that what we are seeing is a combination of outflow and infall on larger scales (see Sects. 3. 3. 1 and 3. 2), with rotation on small scales. Since the radius at which the shift is most prominent in OCS is on the size of the ∼0.
In addition, CH 3 CN does not show the high-temperature spot to the SE of the hot core that is seen in the H 2 CO map, though this is where the CH 3 CN intensity A118, page 15 of 44 A&A 636, A118 (2020) becomes lower so the temperature from CH 3 CN becomes more uncertain due to lower sensitivity. The hot spot in H 2 CO could also be due to chemical effects or enhancement in the outflow, and CH 3 CN likely traces denser conditions, on average, along the line of sight than H 2 CO. Overall, however, the results are broadly consistent, with differences of 30 K around the continuum peak, giving some credence to the higher-temperature regions of the H 2 CO map. From these data it is possible to constrain, in a simplified way, the shape of the temperature profile in the envelope of IRS4. Given the spatial variation and complexity, a spatial average is not possible, but it is possible instead to take a slice in the temperature map along the direction of the filament, averaged over a beam, as shown in Fig.
Chemical abundances In order to proceed in a more quantitative fashion, spectra were extracted at four positions from the WideX data in both the NOEMA-only data which were imaged with the Clark algorithm and the merged NOEMA and 30 m data imaged with the SDI algorithm (see Sect. 2. 3 and Appendix B. 3). These four positions are indicated in Fig. 15 , and were chosen to probe different conditions: the two cold continuum emission peaks emission in the filament, the bright knot which is also detected in the continuum in the direction of the outflow, and the MYSO IRS4. XCLASS fitting was then performed on each spectrum to identify the emission lines and quantify column densities (see Appendix C. 1). Since the continuum is detected towards each position, we can also assume that this gives a good measure of N[H 2 ], using the temperature derived from H 2 CO for that spectrum, and thus obtain absolute molecular abundances.
The resulting map is shown in the left panel of Fig. 17. X[H 2 CO] varies between 2. 4 × 10 -9 and 1. 1 × 10 -7 around the hot core IRS4 with a median of 1. 0 × 10 -8. The median abundance over the rest of the filament is 7. 4 × 10 -10 , though these are upper limits since the H 2 CO data contain the large-scale information but the continuum (used for N[H 2 ]) do not. There is a clear increase in abundance towards the hot core and lower abundances towards the colder continuum peaks, supporting the idea that H 2 CO is indeed formed on the dust grains and thermally desorbed near IRS4. These can be compared with the results of Gerner et al. (  2014 ), who observed a large sample of high-mass star forming regions with the IRAM 30 m and obtained abundances for a number of species, including H 2 CO.
(2014). The values obtained for the filament from the combined NOEMA and 30 m data are roughly in line with the IRDC results from  Gerner et al. (2014). However, the single-dish abundance is a significant underestimate for the hot core and a factor of five higher than the abundance in the filament (cf. similar size-scale issues with temperature in Sect. 3. 2. 6). Gerner et al. (2014)  found that their hot core models produced considerably higher abundances than they observed, but the abundances we obtain for the hot core from the combined NOEMA and 30 m data agree with their models for hot cores at some timescales. Thus the discrepancy found by  Gerner et al. (2014)  between their observations and models for H 2 CO is due to a mixing of conditions within the single-dish beam, which results in an intermediate value between the colder and warmer gas.
Links between spatial scales The CORE data and the analysis presented in Sects. 3 and 4 have shown that the W3 IRS4 region contains a number of structures which span a range of scales and environments. Table  10  gives a qualitative breakdown of the species which trace each physical component of the system, as well as approximate physical properties as determined in the previous sections. While each region can be identified through a combination of physical, chemical, and spatial differences, there is also considerable overlap in properties between them, with no indication of sharp boundaries between clump and filament or filament and core. This suggests that any flow from clump to core scales does not cause a shock, and may instead serve to blur or wash out any transitions. The kinematics do suggest some interplay between the different components along the filament, such that they cannot be treated entirely in isolation from the neighbouring and largerscale structures.
In most cases, we fit isotopologues separately rather so that their ratio can be constrained. After extensive testing, the best-fit solution for most species was found using 300 iterations Notes. ( a) Taken from the Cologne Database for Molecular Spectroscopy (CDMS,  Müller et al. 2001 Müller et al. , 2005)). ( b) Not detected towards W3 IRS4. ( c) Blend of two lines with different quantum numbers but the same E up at the same frequency. The highest A ul is given. of the Genetic algorithm to ensure that the global maximum is found, followed by 100 iterations of the Levenberg-Marquardt algorithm to ensure the best solution within that minimum (see  Möller et al. 2017 , for further details and references). For fits to high spectral-resolution H 2 CO data, this was found to result in unreasonably large pixel-to-pixel variations, while using a 300 iterations of the Levenberg-Marquardt algorithm provided good results, so this latter approach was adopted.
For the fitting of the WideX spectra, each species was fitted separately, starting with the simpler and brighter species, except in cases where some lines are close enough to affect each other, in which case these lines were fitted together simultaneously (for example CH 3 CN and HNCO). Once a good fit was achieved for a given molecule, the solution was held constant dring the fitting of subsequent molecules. Notes. ( a) Taken from CDMS  (Müller et al. 2001 (Müller et al. , 2005)) , except for CH 3 CN and CH 3 OCHO which are taken from the Jet Propulsion Laboratory (JPL) database  (Pickett et al. 1998 ). ( b) Blend of two or more lines with different quantum numbers but the same E up at the same frequency. The highest A ul is given. ( c) Blend of a number of transitions with small differences in frequency due to different spin states but the same E up.
Acknowledgements. The authors would like to thank the anonymous referee for their helpful comments, which improved the paper, the IRAM and GILDAS staff for their help during observations and data reductions,  Thomas Möller  for his technical support with XCLASS,  Arjan Bik  for helpful correspondence about the ionising source for W3 C, and  Ewine van Dishoeck ,  Lars Kristensen ,  Magnus Persson  and  Yuan Wang  for fruitful discussions. J. C. M. , H. B. and A. A. acknowledge support from the  European Research Council  under the European Community's  Horizon 2020 framework programme  ( 2014-2020 ) via the  ERC Consolidator grant  " From Cloud to Star Formation (CSF)"  (project number  648505 ). T. Cs. acknowledges support from the  Deutsche Forschungsgemeinschaft, DFG  via the  SPP (priority programme) 1573 "Physics of the ISM ".
R. K. acknowledges financial support via the  Emmy Noether Research Programme  funded by the  German Research Foundation (DFG)  under grant no. KU 2849/3-1. A. P. acknowledges financial support from  CONACyT  and  UNAM-PAPIIT IN113119  grant, México. REP is supported by an  NSERC   Discovery grant. S. E. R. acknowledges support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement #  706390. This work is based on observations carried out under large project number  L14AB  with the  IRAM NOEMA Interferometer  and the IRAM 30 m telescope. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). This research made use of MATPLOTLIB (Hunter 2007), APLPY, an open-source plotting package for PYTHON ( Robitaille & Bressert   2012 ), and Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration 2018).