dataesr/scientific-paragraphs-categorization huggingface dataset:
The valine to leucine pathway provided more evidence to distinguish the groups: B and D were mostly competent, while C and A were almost entirely deficient. The aspartate to threonine pathway, on the other hand, was present in both C and D group strains, but missing in B strains, thus showing diversity despite that both B and C isolates were from cider. PTSs were identified by searching through the MIC-FAM annotation. However, annotation of PTS is difficult due to their high similarity and because a given PTS can have multiple sugars as substrates. For this reason, we used the Transporter Classification Database to confirm the specificities of the MICFAMs  [45] , as well as the previously described O. oeni PTS proteins  [24]. Five PTSs were complete in almost all strains, which could be considered as the basic set of PTSs (Fig.
Conclusions In this study, we expanded the knowledge of the O. oeni population structure using new genome sequences from cider and kombucha. This led to the integration of two additional phylogenetic groups. Here, we provide evidence to chart their evolutionary history using sequence-based methods and gene absence/presence patterns. The pangenome represents a powerful tool for analyzing strains through a genome browser by synteny to other strains, and by gene classifications like COGs  [61]. This makes it simple to search for strains with specific characteristics. In the future, addition of new, complete O. oeni genomes can easily be compared to the public database to find specific adaptative traits. Several gene clusters in the pangenome subgroups remain to be identified or linked to an actual phenotype.
6-8 Kb and 8-10 Kb fractions were selected using a pulse field program with a 0. 75% cassette. A Covaris E220 machine was used to fragment the DNA prior to Mate-Pair sequencing library construction with the following parameters: target: 500 nt, intensity: 3, duty cycle: 5%, cycles/burst: 200, treatment time: 80s. The libraries were sequenced on an Illumina Miseq with 2 × 250 bp reads. Reads were cleaned with Cutadapt 1. 12  [62] , evaluated with fastQC 0. 11. 5  [63]  and four different assemblers (SPAdes 3. 6. 2 [64] , Minia 3  [65] , Velvet 1. 2. 10 [66], MIRA 4. 9. 5_2 [67] ) that were each tested with different parameters to find the best assemblies, evaluated by the N50 metric. SPAdes with the 'careful' option enabled was chosen to assemble the genomes, and QUAST  [68]  was used to calculate genome assembly statistics.
Public genomes O. oeni genomes (n = 213) was found on NCBI's Genbank. Among these, 142 were reported, but uploaded only as raw reads instead of assembled genomes  [12]. In order to use them in the analysis, we downloaded the sequencing data from NCBI and assembled them, using the same procedure as with our own reads. Of the resulting genomes, 1 was discarded, 130 were assembled by SPAdes 3. 6. 2 and 11 by MIRA 4. 9. 5_2, resulting in a total of 212 public genomes (provided in Additional file. 11: Table  S4 ), along with the non-oeni genomes).
Genome annotation The newly sequenced genomes were annotated using the automatic pipeline of LABGeM's MicroScope service  [70]. Before submission to the annotation service, all Ns and degenerate bases were purged from the genomic sequences to satisfy MicroScope requirements, though this was only relevant for very few genomes. Several algorithms and databases were used for annotation, both for the automatic pipeline and manual curation: Prodigal, Glimmer and AMIgene algorithms for gene detection. SwissProt, TrEMBL protein databases for gene identification. PRIAM EC, MetaCyc Pathways, COGnitor, EGG-NOG and FigFam databases for predicting function. For each gene, the pipeline attempts to identify genes from a set of rules, using BLAST to find similarity in described sequences in the databases.
If computational evidence exists (e. g. similarity in PRIAM EC or FigFam), but no sequence exists in the protein databases, the gene identity is labeled 'putative'. Manual annotation was done by inspecting the combined results from protein databases, functional predictions and synteny information. The combination of sources allowed the curator to infer gene identities and functions in cases where the automatic annotation could not. In order to use the MicroScope genome browser (MaGe) and compare the new genomes to previously assembled sequences, we submitted the 14 new genomes, as well as the public genomes, to the annotation pipeline  [40].
To obtain SNP data, the pangenome of O. oeni was calculated by MicroScope's Pangenome tool  [40]  and 892 gene families were found. Among these, 723 contained no fragmented sequences. They were aligned with a custom script and Clustal Omega  [74]. SNPs and indels (n = 218,180) were identified (excluding 'N's) and concatenated with another custom script. Both scripts were written in python 2. 7  [75]  using Biopython  [76]  and are available in the repository: https://github. com/ marcgall/Genomics-01. Initially, an unrooted phylogenetic tree was constructed using Neighbor Joining and the tree structure was confirmed by bootstrapping (n = 100) (Additional file. 7: Figure  S7 ). To confirm the structure with more robust methods, an unrooted phylogenetic tree was constructed using Maximum Parsimony (which computes distances by minimizing the number of changes) (data not shown1).
Consequently, we call Θ a clock dysfunction metric. P(t | g) g = (g 1 ,…, g G ) There are now several published algorithms which aim to estimate the time at which a transcriptomic dataset was collected using the expression levels of the core clock genes  (17, (23) (24) (25) (26) (27) (28). While these have been used to study aspects of clock dysfunction by calculating a measure for a dataset as a whole and comparing a diseased dataset with a healthy one  (24, 26) , they are not designed to measure clock dysfunction in single samples. The ideas from statistics and information theory mentioned above show that the key quantities determining Θ are the covariance structure of and the way that moves as time t advances (Note S4). Therefore, to measure precision, an algorithm must address these quantities and none of the existing algorithms has been designed to do this (Notes S10.
Moreover, the circadian clock rhythmically controls many molecular pathways which are responsible for large time-of-day dependent changes in drug toxicity and efficacy, whilst clock gene expression is correlated to anti-cancer drug sensitivity in cancer cell lines  (5, 6, 38, 39). Altered expression of clock genes is associated with key oncogenic pathways and patient survival, and the correlation between clock genes and other genes is altered in cancerous tissues  (39). Cadenas et al. (40)  showed that the expression level of some circadian clock genes were associated with metastasis-free survival (MFS) in breast cancer, and discussed the relation with current prognostic factors. They also related gene expression levels (at a single time point that could differ between patients) to clock dysfunction at the dataset level.
They did this by comparing the correlation between the expression of pairs of clock genes for the dataset associated with each prognostic stratum. Shilts et al. (26)  developed two metrics based on comparing how the pattern of Spearman correlations between the clock genes differed in normal and cancerous tissue to conclude that circadian clock progression was In view of these results, it is of interest to apply TimeTeller's ability to measure the functionality of the clock in an individual patient's tumour tissue to determine whether this is a prognostic factor for treatment response and survival. We demonstrate here that TimeTeller can reliably measure dysfunction in single samples and that the dysfunction metric is an independent prognostic factor for both diseasefree survival (DFS) and overall survival (OS) in a large cohort of patients with primary breast cancer.
Results The mouse and human versions of TimeTeller are trained on two different datasets. The human training data set from  Bjarnason et al. ((42) , Note S5. 1) comes from punch biopsies of oral mucosa taken every four hours over 24 hours from five females and five males. This human dataset was chosen because a key initial aim was to develop TimeTeller in order to analyse clock function in the tumour biopsies from the REMAGUS trial  (43)  and our analysis suggested that it was important to match the microarray technologies (Note S9. 2) which in this case was Affymetrix U133 2. 0. The mouse dataset, that we use is from Zhang et al. ((3) , Note S5. 1) and consists of the transcriptomes of 8 mouse tissues measured every 2 hours over 48 hours. The procedure for using these datasets to successfully produce TimeTeller's probability model and likelihood functions is explained in Notes S1-3.
To determine the panel of genes for TimeTeller both the rhythmicity and synchronicity were analysed (Note S7, Table  S1 ). This analysis is essential to ensure the choice of a panel of genes with good circadian rhythmicity combined with minimal variation across the relevant tissues and datasets. It typically produces a panel of between 10 and 16 gene probes and, for the human training dataset, the genes selected are all core clock genes or key clock-controlled genes, including ARNTL (BMAL1), NPAS2, PER1, PER2, PER3, NR1D1 (REV-ERBα), NR1D2 (REV-ERBβ), CIART (CHRONO), TEF and 6 DBP. We call the vector consisting of the combined expression level of these genes the rhythmic expression profile. TimeTeller works on the rhythmic expression profile g and estimates (up to a proportionality constant) an approximation Lg(t) of the likelihood curve P(t|g) discussed above, which for Wild Type (WT) tissue should express the probability that the expression profile g was measured at time t.
In our context this likelihood function is the primary mathematical object expressing the information about clock functionality. To correlate such measurements with clinical disease outcomes such as survival it is necessary to attempt to find associated scalar quantities that summarise the relevant aspects of the likelihood function. We have tested the likelihood ratio functions (LRFs) and this clock dysfunction metric using both  TimeTeller's effectiveness in detecting the efficiency of partial knockdowns of various efficiencies of the central clock gene BMAL1 (ARNTL). We found (Fig. S4A ) that the efficiency of the knockdown was effectively recapitulated by an increase in Θ. We then applied TimeTeller to a number of mouse and human datasets.
Training data provides a range of lower Θ values which is consistent across multiple datasets from healthy tissues. To evaluate TimeTeller's likelihood curves Lg(t), and the corresponding dysfunction metric Θ we firstly tested it on the training datasets using a leave-one-out approach. In particular, we asked if there is a threshold value ΘGCF of Θ such that Θ < ΘGCF can reliably be associated with the good clock function data the mean absolute errors are 1. 32 h and 1. 36 h, respectively. However, in assessing accuracy we need to take account of the fact that there may be variation in clock gene phase across individuals and tissues. An analysis of the human training data shows that much of this apparent error is due to coherent phase variation in the clock genes possibly due to genetic and/or environmental factors.
We hypothesise that low error is due to the fact that in the For the mouse training data the use of the full 48-hour space allows us to observe that TimeTeller's transcriptomic time signature at CTt is essentially the same as at CT(t+24) (Fig. S3 ). This means that there is no significant change to the circadian clock gene shape after the mice have been in the dark for an extra 24 hours. Similar levels of timing accuracy with Θs in the GCF range were found in two mouse datasets from Le Martelot et al. (46)  and  Barclay et al. (47)  (Fig. S4F-H ). Timing for some diseased examples and its relation to second peaks is discussed below.
TimeTeller can identify perturbed but functioning clocks. The gene Nr1d1 (Rev-erbα) is regarded as the main controller of the Zeitgeber time (ZT)18-24 phase of the mammalian circadian clock (1) and knocking out Nr1d1 leaves a functional but perturbed clock when compared to WT mice  (1). Therefore, we applied TimeTeller to an experimental dataset Fang et al. (1)  comparing liver samples of Nr1d1 KO and WT mice entrained to light-dark (LD)12:12 cycles. Since NR1D1 is one of the panel of genes used in TimeTeller it would not be surprising that TimeTeller could distinguish Nr1d1 KO mice from WT mice, and indeed this is the case. Therefore, for this validation, we use a version of TimeTeller that excludes Nr1d1 from the expression profile genes. This modified TimeTeller clearly detects that the Nr1d1 KO mice have a functional but significantly perturbed clock when compared to WT mice (Fig.
Healthy and malignant human tissue have different Θ distributions. In Fig. 3  we show the Θ distributions for a number of datasets. These include healthy datasets which served as controls in the indicated studies and our training datasets, and we observe consistent Θ distribution across all of these. The control Θ distributions can then be compared with two cancer datasets that used the same microarray technology, including oral squamous cell carcinoma from Feng et al. (41)  and breast carcinomas from the REMAGUS trial discussed below  (43, (48) (49) (50). Similarly, in Fig. 3(E, F ) we compare the histogram of Θ values from the training data with that from the patients in the REMAGUS trial. For the human data we use lthresh = -10 or -12. The results presented are for lthresh = -12 but the results for lthresh = -10 are entirely consistent.
TimeTeller likelihood curves have the flexibility to assess functionality. As discussed above and in Note S4 the variance of the sample timing MLE T depends crucially on the variance structure of P(g|t). The above analysis of the TimeTeller likelihood curves shows them to be highly flexible and able to contain interesting biological information. While the ZeitZeiger likelihood curves ((  24 ), Note S13. 2), may be more accurate at assessing timing they are much more uniform (Fig. S5G ) and do not contain this information (Note S10. 4, Fig. S5E, F ) presumably because they are not transparently connected to P(g|t). They have very uniform Θs across the REMAGUS data and are only very weakly correlated with the TimeTeller Θs (Fig. S5 ). While This raises the question of which aspects of the likelihood curves can identify interesting aspects of disease and, in particular, to what extent the presence/absence of a second peak is what is determining the significant hazard ratios found in the REMAGUS data.
, N in G-dimensional space. The projection operator described in Note S2 gives an optimal way to linearly project these points into d-dimensional space for all d < G. This produces a corresponding set of N d-dimensional points Q i,j , j = 1,. , N. We then fit a multivariate normal distribution (MVN) to the points Q i,j. The dimensionality d is chosen so that there are enough vectors Q i,j to fit a d-dimensional multivariate Gaussian (using the MATLAB function fitgmdist) while ensuring that most of the variance in the data is captured by the d-dimensional projection. In our case we take d = 3. A MVN distribution is defined by its mean and covariance matrix which we denote by µ i (t i ) and Σ i (t i ) respectively. We fit a shape-preserving smoothing cubic periodic spline through the mean vectors µ i (t i ) and each of the six entries that determine the 3 × 3 symmetric matrix Σ i (t i ) so as to extend µ i (t i ) and Σ i (t i ) to all times t between the time points, thus obtaining µ i (t) and Σ i (t).
A piecewise cubic Hermite interpolating polynomial spline is used in this case. This type of spline is shape preserving, i. e. continuity of the second derivative is not obligatory. This is suitable as, for example, if two covariance matrix entries were identical for two consecutive time Gaussians, the Hermite spline allows the value of the joining spline to stay the same in the space between, while a standard spline would enforce some change. This spline also interpolates so that it passes through all points. The calculations were carried out using the MATLAB function pchip. Using this approach, for this value of i, we have determined a family of MVN distributions for all times t between the first and last data times. S1. 2 L g (t) and the log threshold l thresh Now we define the likelihood curve L g (t) where g is an expression profile and X is the G-dimensional vector obtained from g after normalisation.
As a result of this modification, if the maximum value of log L g (t) is only just above l thresh , then it and the corresponding likelihood ratio curve will have intervals on which they are flat. This is, for example, the case for some of the curves in Fig. S2  below. X t 1 X t 3 X t 4 X t 6 X t 5 X t 2 U Q t 4 Q t 1 Q t 5 Q t 2 Q t 3 If this is the case then the length of these flat intervals above the curve C(t|T ) defined below in Note S2 contributes to Θ. This contribution has interesting information in it because it is related to how low the maximum value of L g (t) is. However, since the size of this contribution depends upon the particular choice of l thresh we wish to avoid this and so we generally choose a value of l thresh that for the datasets under consideration keeps the numbers of samples with this issue as small as possible.
The curve has been centered so that the maximum is at t = 12 to enable easier comparison of the curves and the associated Θ value. F-H. Examples of centered likelihood ratio (LR) curves from the mouse. In these l thresh = -7. Here l thresh = -12. I. The LR curves from the Bjarnason human data. J-L. The curves for the REMAGUS data with Θ < 0. 1 (J),0. 1 < Θ < 0. 3 (K) and 0. 3 < Θ (L). For the last dataset the flat part of the LR curves intersect the C(t|T ) curve (black) and therefore the Θ values are significantly affected by this. This occurs because the maximum value of the corresponding likelihood curve is close to the threshold value l thresh used. To overcome this one can decrease l thresh. Calculate ℓ p (t) = log P (g|t) for the MVN distributions P (g|t) given by µ p (t) and Σ p (t).
S5 Data sets Experimental setup. 6 wk-old male C57/BL6 mice were entrained to a 12h:12h LD schedule for 1 week, and then released into constant darkness (CT0). The mice were provided with food and water ad libitum. From CT18 post-release, 3 mice were killed every 2 hours, for 48 hours (until CT64), and specimens from 12 organs were snap frozen in liquid nitrogen. Microarray methods. RNA was extracted and pooled for three mice for each tissue and time point. RNA abundances were quantified using Affymetrix MoGene 1. 0 ST arrays using the standard manufacturer's protocol. The results of this make up the. CEL files that are used in this study. They are accessible via GEO accession number GSE54652. In their analysis, Zhang et al. used the RMA algorithm to normalise the data using the Affymetrix Expression Console Software.
As is standard, GeneChips were matched to genes and filtered for protein-coding status, resulting in 19,788 genes forming the background set. They performed oscillation detection using the R package JTK CYCLE (3) to fit time series data and detect oscillations on transcripts, using a the cutoff significance of q<0. 05 (5% FDR). Human: Oral Mucosa Timecourse microarray data  (Bjarnason et al. )  For this study Bjarnason et al. (4) recruited ten healthy human volunteers, five female and five male. Mucosa tissue was collected at six time points: 8 am, noon, 4 pm, 8 pm, 12 midnight, and 4 am. Subjects were selected after screening by clinical history, physical examination, routine blood work (complete blood count, electrolytes, creatinine) and actigraphy to confirm regular sleep-wake patterns.
Following fragmentation, 15 µg of cRNA were hybridized for 16 hrs at 45 • C on GeneChip Human Genome U133 Plus 2. 0 arrays. GeneChips were washed and stained in the Affymetrix Fluidics Station 450 and were scanned using the Affymetrix GeneChip Scanner 3000. Although there were 16 probes identified in Note S7 as rhythmic and synchronised, we only use 15 probes going forward. The reason is that the Per1 probe 244677 at was found to have significant signal issues in many of the independent human datasets, i. e. the signals values were very low. As there is another Per1 probe in this dataset that does not have this problem, we can conclude that this is a probe issue, and not an issue with the Per1 gene expression.
S5. 2 Other mouse datasets We also consider the following datasets which are comparable because (i) they use the same male mouse C57BL/6 and (ii) all microarray expression analysis used the Affymetrix MoGene 1. 0 ST GeneChip. • LeMartelot et al. (5). This is made up of microarrays from pooled RNA extracted from the whole liver of 5 mice. Samples were collected at times ZT2, ZT6, ZT10, ZT14, ZT18, ZT22, ZT2(+24) after the mice had been entrained to LD cycles for 2 weeks. Accessible via GEO accession number GSE35789. • Fang et al. (6). 5 Wild Type and 5 Rev-erbα KO mice were entrained to LD cycles and euthanised at ZT10, where liver samples were taken. As the training data is labelled between CT18 and CT64, this corresponds to CT34 in TimeTeller. The authors report that the knock-out was successful.
S11 group of stressed mice were kept awake during the first 6 hours of light (ZT 0-6) on days 1 through 5 and days 8 through 12 using a gentle handling approach designed to minimize stress effects and intervention by the experimenter. At all other times mice were left undisturbed, and the other group of mice were left completely undisturbed. Liver and adipose samples were taken from 3 mice in each of the 2 conditions at ZT1, ZT7, ZT13, and ZT19. Accessible via GEO accession number GSE33381.
S5. 3 Other human datasets Working with human data raises extra issues. The training set is smaller, and we are not working with genetically identical humans. All of the data we consider is from live tissues, and not in vitro samples as we expect this to be appropriate for meaningful comparison with diseased tissues. So far as we are aware the only 24h timeseries for biopsies from healthy human tissue using the U133 The control data for this study includes 5 healthy control oral mucosa samples from the UK and 3 healthy control samples from individuals in Sri Lanka. The study states that the samples were handled using identical protocols for tissue collection and processing 2. All individuals were healthy, with low risk factors for cancers of the mouth, but there is no other information given on the individuals or their lifestyles.
Accessible via GEO accession number GSE51010. • Boyle et al. (9)  studied the effects of smoking on the oral mucosal transcriptome. Forty current smokers and forty age and gender matched never-smokers underwent buccal biopsies. Eligible subjects were healthy volunteers (except for the effects of smoking in the smoker cohort). Since one sample was excluded from the study based on a quality measure we have 79 samples for analysis. Accessible via GEO accession number GSE51010. • Feng et al. (10)  conducted a comparative analysis of healthy oral mucosa transcriptome and oral squamous cell carcinoma (OSCC) transcriptome. The dataset contains 229 samples in total, 167 of OSCCs, 45 of normal oral mucosa, and 17 samples are of dysplasic oral mucosa tissue. Dysplasic tissue is abnormal tissue that could signify early signs of cancer.