dataesr/scientific-paragraphs-categorization huggingface dataset:
Using a real example from a molecular phylogenetic dataset, we show that contaminants, if not eliminated, can have a decisive, deleterious impact on downstream comparative analyses. Conclusions: Cross contamination is pervasive in new and published datasets and, if undetected, can have serious deleterious effects on downstream analyses. CroCo is a database-independent, multi-platform tool, designed for ease of use, that efficiently and accurately detects and removes cross contamination in assembled transcriptomes to avoid these problems. We suggest that the use of CroCo should become a standard cleaning step when processing multiple samples for transcriptome sequencing.
Background Contamination between nucleic acid samples has long been recognised as a potential problem in molecular biology. The use of amplification by polymerase chain reaction (PCR) and, more recently, high-throughput sequencing, implies that even very low levels of contaminating nucleic acids, regardless of their sources, can be sequenced at sufficient coverage to be present in downstream datasets  [1] [2] [3] [4] [5] [6] [7] [8] [9]. Various tools have already been developed in order to discriminate between the sequences of the organism of interest and contaminant sequences originating from parasites, gut bacteria, endosymbionts or the environment. These algorithms usually identify the contaminant sequences based on specific criteria and infer the taxonomic source of the contaminant using a reference database.
The Blobtools pipeline  [10]  detects contaminant sequences based on their GC content, read coverage and taxonomic assignment (using Basic Local Alignment Search Tool (BLAST) against the National Center for Biotechnology Information (NCBI) non-redundant database). A slightly different method, Anvi'o  [11] , first automatically bins contigs based on read coverage and/or k-mer frequencies, then identifies the contaminant bins. Lastly, the algorithm Model-based Categorical Sequence Clustering (MCSC)  [5]  uses a clustering method based on the frequent patterns observed in the sequences (divisive hierarchical clustering) and then identifies the contaminant clusters by blasting against the Uni-Ref90 database. These methods (except for MCSC) focus on genomic data; however, they partially rely on public databases that are not always devoid of contamination and are designed to detect contamination from distant organisms.
As transcriptomic data are currently widely used in evolutionary biology, we designed a new tool, CroCo, designed for RNA sequencing (RNA-seq) data; it relies on expression level estimates, it is reference-free, and it targets another type of contamination: cross contamination. Cross contamination is defined as contamination across samples handled in parallel in a given sequencing project. It is of experimental origin and can potentially arise at multiple benchwork steps: sample handling, DNA/RNA extraction, library preparation and amplification, sample multiplexing and inaccurate barcode sequencing. Our empirical observations show that some amount of cross contamination seems unavoidable when multiplexing complementary DNA (cDNA) libraries for high-throughput transcriptome sequencing of multiple species (for instance, for subsequent phylogenetic tree reconstruction).
Methods In order to determine whether a given sequence in a sequencing experiment involving multiple species is likely to be a contaminant, we have developed a procedure that uses transcript quantification estimates. Our approach is independent from any public database. In short, a sequence present in the assembled transcriptome of species A will be considered a contaminant if the same sequence is represented by a higher number of reads in another species B from the same study. This procedure relies on the following assumptions: (1) contamination is likely to derive from messenger RNAs (mRNAs) that are at high concentration in the source species (both because these are more likely to contaminate and because successfully assembling a transcript requires sufficient read coverage); (2) contaminating molecules are expected to be found in lower quantities in the contaminated sample than in their sample of origin;  (3)  the ratio between levels of contaminator and contaminated sample should hold regardless of the origin of cross contamination (e.
g. tissue/RNA contamination at the bench, mixed tag during double PCR, contaminations during manufacturer's proprietary protocols), the determination of which is not the focus here (but see  [9] ). It is therefore expected that reads from contaminating nucleic acids will be found at much lower levels in the contaminated sample than in the contaminating source. Here we present CroCo, a cross contamination detection and removal tool based on these expectations, which expands on preliminary pipelines used in two independent recent studies  [15, 16]. CroCo uses sequence read files and assembled transcriptomes from each sample included in a given sequencing project involving multiple species. First, a BLASTN step across all pairwise transcriptomes defines a list of transcripts suspiciously similar (at the nucleotide level) across samples.
By default, two transcripts in two different samples that are > 95% similar over a fragment of > 40 nucleotides are considered suspicious. This criterion is valid as long as the genetic distances between all species are sufficiently high (see below). All assembled transcriptome files are then concatenated into a reference metatranscriptome for subsequent estimations of their expression levels. For a given suspicious transcript, CroCo quantifies its expression level N (in transcripts per million, TPMsee  [17] ) in reads from each sample. A transcript present in the assembly from sample A but expressed in another sample at a higher level (that passes a user-defined threshold) is considered a contaminant. CroCo performs this comparison for all suspicious transcripts from all samples. Given three user-defined parameters corresponding to (1) a fold difference value (X, default = 2); (2) a high expression level (Y, default = 300 TPM); and (3) a low expression level (Z, default = 0.
Analyses of six recent sequencing projects We selected six datasets to be used as proof of concept for the cross contamination detection strategy implemented in CroCo (Additional file 1: Table  S1 ). Note that the two datasets published in the context of the present study have been entirely processed by different authors in different labs; hence, they were analysed as two separate datasets. Transcriptomes from dataset C were assembled as described elsewhere  [18]. Transcriptomes from dataset D were assembled using SOAPdenovo-Trans  [19] , and transcriptomes from dataset E were assembled using Trimmomatic  [20]  and Trinity v2. 1. 1  [21]. CroCo was used with default parameters (-f 2 -c 0. 2 -d 300), and the results of these analyses are shown in Fig. 1.
Impact of cross contamination on phylogenomics In order to test the impact of cross contamination at a phylogenomic scale, we retrieved the 114 genes from a previous study  [22] , from which we kept only Pleurobrachia species sequences. To these 114 reduced alignments we added the raw ctenophore transcriptomic data from that study (i. e. dataset A, for which the cross contamination network is shown in Fig. 2a ) using Forty-Two (available at https://bitbucket. org/dbaurain/42/). We then used SCaFoS  [23]  to concatenate the 114 completed alignments into a supermatrix, selecting the longest sequence if several sequences were present for a given species. We used RAxML  [24]  with the LG + Γ4 + F model of sequence evolution to infer phylogenetic relationships among ctenophores (Fig. 2b ). We then used CroCo (using default parameters) to clean the raw transcriptomic data, and we retained all transcripts categorised as clean.
Example of cross contamination in a single-gene phylogeny To exemplify the ability of CroCo to detect cross contaminations and their possible dramatic impact on gene phylogeny reconstruction, we arbitrarily selected a contaminated transcript from dataset A (i. e. transcript 'sb|373879' from the Vallicula multiformis transcriptome) and we used USEARCH  [25]  to extract homologous sequences from other transcriptomes from the same experiment (the parameters are as follows: -usearch_global -id 0. 6 -maxhits 10000 -maxaccepts 10000 -maxrejects 10000). All sequences were then aligned with Clustal Omega  [26]  as implemented in SeaView  [27]  using default settings; the phylogenetic relationships between these transcripts were inferred by running 100 maximum likelihood searches under the LG + Γ4 + F model and 100 bootstraps with RAxML.
Cross contamination simulation experiments Simulation of divergent transcriptomes In order to test the accuracy of our procedure as well as to compare the behaviour of the different mapping/ quantifying tools with closely related samples, we selected one reference transcriptome for subsequent simulations (Austrognathia sp. from dataset F; 31,529 transcripts). The abundance of each transcript was estimated in transcripts per million (TPM) using RSEM-Bowtie  [28]. Based on this reference transcriptome, we simulated 10 divergent transcriptomes (of 31,529 transcripts each) using a Python script which randomly mutates nucleotides at a specified rate (divergence level ranging from 1 to 10%). The abundance of each divergent transcript is directly derived from the reference transcriptome. For each level of divergence, we worked on a pair of species including the reference and a divergent transcriptome.
Estimating effective contamination probabilities To explain the cross contamination simulation procedure, we now focus on one such pair: the reference transcriptome and the 10% divergent transcriptome (hereafter 'ref ' and 'div10' , respectively). A given transcript originating from sample A which is transferred into sample B has a given probability to be effectively   S1 ; references  [16, 22, 34, 41]   Fig. 2  Dramatic effect of cross contaminations on reconstructing ctenophore relationships using a phylogenomic dataset. a Cross contamination network for dataset A as reconstructed by CroCo. Nodes and links represent transcriptomes and cross contaminations, respectively. Only transcripts strictly categorised as cross contaminants are taken into account here. Node sizes are proportional to the number of times the node is the source of cross contamination, and node colours represent the percentage of contaminated transcripts in the transcriptome.
For clarity, weak links, defined as less than 2% of the strongest link in the network, are not shown. b, c Ctenophore phylogenetic relationships reconstructed with 114 genes using (b) untreated transcriptomes and (c) transcriptomes cleaned using CroCo (see details in Methods). In (b) the placement of lineages highlighted in orange disrupts the monophyly of the clade 'Lobata' (here represented by Mnemiopsis leidyi and Bolinopsis infundibulum). With the cleaned dataset (c), the same lineages, in blue, are placed in agreement with recent studies of ctenophore relationships  [16, [31] [32] [33]. The two dotted arrows and their corresponding numbers indicate two major cross contamination events that can be observed on the cross contamination network (a) sequenced and assembled into a contaminating transcript in the transcriptome of sample B.
For a given transcript, this probability should depend on the number of copies transferred (i. e. mRNAs) and thus on its abundance in sample A. These 'effective contamination probabilities' have been estimated by generating a virtual pool of one million mRNAs from the 'div10' transcriptome (based on the TPM of each transcript) in which we randomly sampled 10,000 mRNAs (corresponding to about 7300 unique transcripts). These transferred transcripts were added to the 'ref ' transcriptome, and then 20 million reads were simulated from this contaminated transcriptome (as detailed in the following section). With a BLAST approach, we estimated how many of these transferred transcripts were recovered after transcriptome assembly with Trinity v2. 4. The 'effective contamination probabilities' ranged from 48.
For each sample ('ref ' contaminated and 'div10'), 20 million paired-end reads were then simulated with the R package polyester  [29]  with a uniform error of 0. 5% (following  [30] ). The number of simulated reads for each transcript was directly based on its TPM. For each divergence level (n = 10) and contamination strength (n = 2), CroCo was run with the three mapping tools (RapMap, Kallisto and Bowtie) using default parameters. The results are shown in Additional file 1: Figure  S3.
Benchmarking CroCo with biological data We categorised dataset E transcripts with CroCo (default settings) and then incorporated all transcripts into reference alignments maintained and updated by author HP and including taxonomic sampling corresponding to dataset E (see  [13] ), using the Forty-Two software package (https://bitbucket. org/dbaurain/42/downloads/). The taxonomic sampling already present in these alignments allowed us to determine the true origin of every transcript, which we compared to CroCo categorisation in order to evaluate its accuracy. The results of this comparison between manual and automated detection of cross contamination are shown in Additional file 1: Figure  S2.
Detecting cross contamination in six transcriptomic datasets We used CroCo on six recent RNA-seq datasets, including two of our own, as a proof of concept (see details in Methods and Additional file 1: Table  S1 ). These datasets correspond to the sampling of several different species spanning the diversity of a given group (e. g. Metazoa, Ctenophora, Platyhelminthes, Streptophyta). Within a given project, the mRNA of each species was sequenced by a single group of authors and hence potentially handled at the same place. We found that all of them were affected by cross contaminations and, more worryingly, that these cross contaminations can reach dramatic levels (i. e. almost 30% of all transcripts in one species, see Fig. 1 ). Sequencing experiments differ greatly in their levels of contamination (compare the highly cross contaminated datasets A and C with the cleaner datasets B and E in Fig.
1 ). The level of cross contamination also varies (sometimes greatly) between samples within each experiment, as is obvious when comparing Euplokamis dunlapae and Mertensiidae sp. in dataset A or Lampea pancerina and Liriope tetraphylla in dataset B (Fig. 1 ). This is consistent with some cross contamination stemming from experimental mishandling which is expected to result in preferential cross contamination patterns (i. e. only certain samples contaminate or are contaminated). These patterns can be easily observed on cross contamination network graphs rendered by CroCo which can be used to trace back the likely experimental step(s) at the origin of cross contamination event(s) (see Fig. 2a ).
Gene phylogeny in the presence of cross contamination In order to illustrate both the problems caused by cross contaminations for phylogenetic studies and the ability of CroCo to detect them, we built a singlegene tree based on ctenophore (comb jellies) sequences from dataset A  [22]  prior to CroCo use. We then used CroCo to detect cross contaminations and correspondingly annotated the sequences in the tree (Additional file 1: Figure  S1 ). Multiple cross contaminations clearly hampered the interpretation of the correct evolutionary history of this gene, but they were all detected by our procedure (i. e. none of them was categorised as clean). After their removal, we could reconstruct a gene phylogeny that is congruent with expected relationships between these ctenophore species (see Additional file 1: Figure  S1  and  [31] [32] [33] ).
Evaluating the accuracy of CroCo To further illustrate the accuracy of our approach, we used cross contaminations that have been previously identified as a reference  [13]. Indeed, the potential negative impact of cross contaminations on phylogenomic inference has been studied using a previously published dataset  [34]  by comparing trees obtained before and after manual removal of contaminants. We thus evaluated CroCo's ability to detect cross contaminants. Across all species, CroCo was able to detect 97. 2% of the cross contaminations that have been identified manually (i. e. 174 out of 179 cases) while wrongly discarding < 1% of correct sequences (i. e. 6 out of 629 transcripts, see Additional file 1: Figure  S2 ). We also evaluated CroCo's accuracy by analysing simulated datasets with cross contaminations between transcriptomes that we made increasingly divergent from each other (from 1 to 10% divergence).
CroCo as a cleaning tool for phylogenomics As previously shown, cross contamination can be deleterious even at a phylogenomic scale where data quantity might be expected to be sufficient to overcome the erroneous signal from contaminants  [13]. This happens because some cross contaminations are not random (see preferential cross contamination patterns in Fig. 2a  and Additional file 1: Figures  S4-S8 ) and can therefore create an additive non-phylogenetic signal that is reinforced as more and more genes are added (a case of systematic error). To assess the benefits of CroCo for phylogenomic analyses, we compared results from the supermatrix of 114 genes from the heavily contaminated ctenophore species of dataset A (Figs. 1 and 2a and Additional file 1: Figure  S1 ) before and after cross contamination cleaning (see Methods).
In the ctenophore tree reconstructed with raw transcriptomic data (Fig. 2b ), the Lobata species Bolinopsis infundibulum is placed close to Mertensiidae sp. , while the other representative of Lobata, Mnemiopsis leidyi, branches together with platyctenids (Vallicula and Coeloplana), both with maximal support. These relationships are at odds with ctenophore relationships previously derived from singlemarker molecular phylogenies as well as with morphology  [16, [31] [32] [33]. One of these problematic relationships has been recovered with maximal support in four recent studies based on dataset A  [22, [35] [36] [37]. These two major incongruences match the two largest cross contamination events detected by CroCo in this dataset (events 1 and 2 marked in Fig. 2b , respectively corresponding to 27,475 and 46,473 cross contaminated transcripts; see black thick arrows in Fig.
Discussion A quantitative tool to classify transcripts CroCo has been designed to detect cross contamination in transcriptomes assembled from RNA-seq data of samples that were either multiplexed during sequencing or, more generally, handled by the same people or at the same location. Although we found evidence for systematic and pervasive cross contamination events, understanding CroCo's strategy is important in order to interpret its categories correctly. Our approach is quantitative and relies solely on differential transcript quantification patterns to determine the origin of a given transcript. This allows CroCo to be reference-free and portable to any transcriptomic dataset, but it also renders its results sensitive to the accuracy of quantification estimates and to the user-defined parameters for categorisation.
The quality of both reads and transcriptome assemblies is important to maximise the accuracy of expression level estimates. For example, it is expected that transcriptome redundancy such as natural transcript variants might create false positive results, since they likely display different expression levels. Additionally, issues during transcriptome assembly might result in chimaeric transcripts that cannot be handled appropriately by CroCo. Data quality variation might explain most of the varying percentage of 'low coverage' transcripts in different datasets. The variation between dataset D (i. e. 454based) to the others (i. e. Illumina-based) in that regard is certainly due to the sequencing technology used. Note that even considering that 454 pyrosequencing data are less adequate than Illumina data to quantify transcripts, this did not hamper CroCo from accurately detecting more than 97% of validated cross contamination cases.
CroCo parameterisation Read mapping strategies and their implementation are an ongoing research area, leading us to implement three methods for transcript quantification in CroCo: Bowtie  [38] , Kallisto  [39]  and RapMap  [40] , which rely, respectively, on alignment, pseudo-alignment and quasimapping approaches. Using RapMap for transcript quantification yielded the most accurate results when analysing simulated cross contaminated datasets (Additional file 1: Figure  S3 ). We thus set RapMap as the default quantification tool in CroCo, and advise the user that Bowtie should not be used for between-sample nucleotide divergence of less than 3%. CroCo uses three parameters (see Implementation) to govern the categorisation of transcripts which can all be set by the user. If the 'fold difference' X parameter value is set low, CroCo will categorise more transcripts as either clean or as cross contaminated, whereas higher values will increase the number of dubious transcripts.
The parameter Y is a quantification threshold above which a transcript is considered to be highly expressed. If a given transcript exceeds that threshold in three or more samples, which represents an unexpected and suspicious pattern, CroCo will categorise it as 'over expressed'. Lastly, the parameter Z is the quantification level threshold under which a transcript is considered rare. A transcript found to be rare in every sample under study will be categorised as 'low coverage' , under the rationale that our quantitative approach lacks power to confidently determine their true source dataset. Setting a combination of a high fold difference value, a low over expression threshold and a high low coverage threshold will result in high confidence cross contaminated and clean transcript assignments at the price of discarding a larger amount of data (see Additional file 1: Table  S2 ).
Caveats when using CroCo CroCo is designed to identify transcripts that conform to the expected profile of cross contaminations, but the user must use this information carefully. First, different decisions may be made on how to interpret or use the output depending on whether, for example, the user wants to avoid any possibility of cross contamination versus minimising the chance of discarding useful information. There are three inherent limits to the approach implemented in CroCo. First, the expression level is expected to correlate with the quantity of input biological material. This implies that CroCo will not be able to detect cross contamination that occurred before any normalisation of the cDNA libraries under study. This also advocates for preferring sequencing data that allow more accurate estimates of transcript quantification (e.
g. preferring Illumina data over 454 pyrosequencing). A second obvious limitation is that if the species from which a given contamination originated is not included as input for CroCo, it will never be detected. Since a majority of cross contamination events can happen at a sequencing facility  [9] , we highlight the need for improved transparency in multiplexed taxon sampling from both researchers and sequencing service providers. The third limitation is that the organisms under study must not be too closely related: the more closely related the samples are, the more difficult it will be to identify cross contamination. The method will fail when handling samples from individuals of a single species (e. g. comparing human samples). This limitation could be somewhat alleviated by setting more stringent values for parameters governing the BLAST step that determines the list of suspicious transcripts (see the user manual for additional recommendations).