dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to identify geographic population structure in the DBLα domain we show that utilizing methods designed specifically for the investigation of var gene evolution provides substantially more insight than more naive approaches such as a binary matrix of presence/ absence of types. Using the JHMM method  [43] , we were able to distinguish all countries within the global dataset. We describe multiple sub-populations in South America consistent with a previous analysis  [39] , and further support the "Out of Africa" hypothesis where P. falciparum was introduced into South America from Africa, likely due to the trans-Atlantic slave trade  [31]. Interestingly, we found that the South American isolates were not more or less related to any isolates from any of the African countries, which may indicate that we have not sampled DBLα types from the "origin" populations out of Africa, or that the South American sequences have diverged significantly from African sequences due to e.
Whilst we cannot ascertain their function, the high prevalence and strong conservation of these DBLα types on a global scale could indicate they have an important biological function and warrant further attention. Biological function is not the only explanation for conservation of some of these DBLα types. For example, eight of these 100 conserved DBLα types were associated with selective sweeps on chromosomes 4 and 7 of alleles that confer antimalarial resistance to pyrimethamine and chloroquine, respectively  [5]. The conserved var gene associated with a selective sweep of four var genes in the subtelomeric region of chromosome 6 by Otto et al. (2019)  [5]  and related DBLα type conserved in our dataset has no apparent association with antimalarial resistance. Other as yet unidentified selective sweeps of alleles advantageous to the parasite could be responsible for some of the conserved DBLα types.
We have demonstrated the power of using the JHMM method to investigate var gene populations. However, the method is computationally expensive and can take days to run on large datasets. With the increasing size of datasets and for this approach to be useful in larger geographic surveillance studies, its computational performance will need to be improved. A large database of geographically assigned DBLα types analyzed by the JHMM for geographic signatures would be the goal to underpin a cost-effective surveillance method in a manner similar to influenza global surveillance with hemagglutinin sequences. In describing the global population structure of P. falciparum DBLα types and their relationship to other Plasmodium species, we have demonstrated the geographic variability of var population structure despite the incredible diversity and high recombination rate.
Jumping hidden Markov model We used the implementation of the jumping hidden Markov alignment model (JHMM) (Mosaic) algorithm kindly provided by  Zilversmit et al. (2013)   [43]  to estimate the posterior likelihood of each unique DBLα type being related at each position to any other type in the dataset after first translating the centroid sequence for each DBLα type into its corresponding protein sequence. Here, the centroid is defined using the USEARCH clustering algorithm with all sequences in a DBLα type being within 96% pairwise identity of the centroid. Briefly, the model combines the pair-HMM model of pairwise sequence alignment  [63]  with the probabilistic model of  Li and Stephens (2003)   [64] , which describes the impact of recombination on haplotype sequence diversity. The resulting model is very similar to that used in fineStructure  [65].
(2013)   [43]. The exact commands used to run the Mosaic algorithm are provided in the "supplementary_-methods_2. Rmd" file on GitHub, along with the the Mosaic source code, at: https://github. com/gtonkinhill/global_var_manuscript. Due to the large number of DBLα types in our dataset, a number of steps had to be taken to deal with the increased computational complexity involved. Initially the DNA sequences from the centroid of each DBLα type were translated into their respective protein sequence and removed if the resulting protein sequence contained a stop codon. Alignment of protein sequences has previously been found to be more sensitive for the analysis of var genes  [14]. The DNA sequences that could be translated were then clustered at 96% identity using USEARCH as described previously  [66].
The protein sequences that corresponded to the centroids of these clusters were used in the JHMM after trimming to a consensus alignment using Gismo  [49]. To train the non-jump parameters of the model, we implemented a script to run the Viterbi training algorithm  [63]  with the jump probability set to "0". This was used in place of the Baum-Welch algorithm used by  Zilversmit et al. (2013)   [43]  as it is more efficient and it was not feasible to run the Baum-Welch algorithm on our dataset. The non-jump parameters were then fixed, and a composite-likelihood surface was generated for the jump parameter by searching a randomly selected subset of 1000 sequences against the entire dataset. This significantly reduced the computational time required. The maximum-composite-likelihood estimate for the jump parameter was then used in the analysis.
As each translated DBLα type was only represented once in the model, we had to account for this when constructing the expected country mixture proportions for each isolate. The proportion of each isolate that came from a particular country was found by summing the posterior likelihood that an amino acid position originated from a sequence in that country and normalizing by the total sequence length of that isolate. In more detail, let R t be the set of DBLα type centroids in the target isolate (T), R S the set of DBLα type centroids in an alternative isolate (S), r tl be the l th amino acid in a centroid t from the target isolate, L r t the length of centroid r t , k be the set of all isolates in country k and X TS be the proportion of amino acids inherited by T from S. Then, E X TS ½ � ¼ P r t 2R T P l2L r t P r s 2R S Pðr tl is from r s Þ P r t 2R T L r t Here, if the target centroid was a singleton, P(r tl is from r s ) is taken as the posterior probability identified from the JHMM algorithm normalized by the number of times the centroid S was found in the full dataset.
If the target DBLα type was found multiple times in the dataset, P (r tl is from r s ) = 0 if the DBLα types are not identical and 1/(number of identical centroids) otherwise. The algorithm was tested with identical DBLα types present and found to always split the posterior probability evenly between the identical copies. Finally, the expected proportion of an isolate most closely related to a country was found by averaging over the respective matches to isolates in that country. That is,.
Binary analysis The DBLα sequences were clustered using a pipeline based on the USEARCH v8. 1. 1831 software suite  [66]  as described previously in Ruybal-Pesantez et al. (2017)  [40]. Specifically, the derep prefix command was used to sort the sequences based on the number of duplicates present before redundant sequences were removed. The remaining sequences were then clustered using the cluster_fast command at 96% pairwise identity. A binary presence/absence table was generated by searching the original sequences against the centroids from the clustering using the usearch_global command. The resulting matrix was forced to be binary by setting any entry greater than "0" to "1". Isolates were removed if they had less than 20 DBLα types and remaining singleton types were removed from the matrix.
This threshold was chosen as it has been found previously to filter out poor quality isolates  [39, 40]. To test the robustness of our results to this threshold, the binary analysis was repeated with a threshold of two DBLα types. The resulting t-SNE plot is given in S17 Fig and indicates that the population structure is still clear. The t-SNE and principal component analysis (PCA)  [48, 67]  were performed using R  [68]. The matrix was then converted into the format expected by Admixture v1. 3  [69]  where a present type was represented as an alternative allele in a haploid chromosome. Finally, a phylogenetic tree was constructed by treating each isolate's corresponding row in the matrix as its binary sequence. RAxML v8. 2. 8 was then run using the BINCAT model. The code as well as a brief description of the methods is available on GitHub at: https://github.
Feature Frequency Profile (FFP) In the Feature Frequency Profile (FFP) analysis, we used a reduced RY alphabet to describe the gene sequences for each isolate, where R stands for the purine bases (AG) and Y stands for the pyrimidine bases (TC). This alphabet was chosen to reduce memory usage as suggested in  Sims et al. (2009)   [52]. To choose an appropriate k-mer length, we first looked at word usage to obtain a lower bound. As suggested in the FFP manual, we counted the number of times kmers appeared at least twice in the dataset for different values of k. The peak of this distribution occurred at a length of 17 (S13 Fig). K-mers shorter than this are very commonly found and thus offer little additional distinguishing information. By investigating the relative entropy between the observed frequency of a k-mer and a k-2 Markov model  [52] , we can also obtain an upper bound for the k-mer length.
When the relative entropy is small, this indicates we can predict the frequency of k-mers from smaller k-mers  [52]. Consequently, this gives an upper bound on k which we found to be approximately 22 (S14 Fig). Thus, a choice of k = 20 was sensible. The frequency of all 20-mers was then calculated for each isolate after converting their sequences into the reduced RY alphabet. A distance matrix was generated using Jensen-Shannon divergence. The FFP v3. 19 program was used to estimate the upper and lower bounds for k, based on the 3D7 isolate sequence. The distance matrix was then generated using a custom Python script, before FastME v2. 1. 4 [70]  with default settings was run to produce a neighborjoining tree. The final tree diagram was generated using the R package ggtree  [71].
Comparison to a previously published global assembly of var genes The 100 high-frequency DBLα types identified in this study were used to BLAST query a previously published global assembly of var genes  [5]. The predicted PfEMP1 domain structure of the hit and the subject sequences were extracted and ranked by E-value (Note. All low E-value sequences were visually inspected for conservation). Conserved structures are indicated in S1 Table. The accession numbers of the conserved assembled var genes that occurred in clusters associated with selective sweeps for regions of chromosomes 4, 6, 7, 8, and 12 were obtained from the author  [5]. DBLα types that had a BLAST subject hit that was contained in these selective sweep clusters were identified and are indicated in S1 Table.
Africa Iran. 45 uncomplicated P. falciparum isolates were collected between 2000-2003 from Iran and are further described in  Yalcindag et al. (2012)   [31]. PNG. In contrast to the previous datasets, the sequences obtained from PNG were not processed using the same bioinformatic pipeline. The PNG sequences were generated using Sanger sequencing after first amplifying for DBLα domains using the same DBLα primers as were adapted for the 454 sequencing  [37]. The isolates were sampled from two geographically distinct areas in PNG (Wosera/Mugil) and resulted in var DBLα sequences from 33 and 35 isolates, respectively. A more detailed description of the experiment, sampling and bioinformatic pipeline can be found in  Tessema et al. (2015)   [38]. sequence identifier (shortID); the number of BLAST hits to reach 96% pairwise sequence identity (Hits); the countries the sequence was found in from the current dataset (Countries_binary); the additional countries the sequence was found in through the BLAST analysis (Countries_blast); whether any of the common BLAST hits were annotated as pseudogenes (Annotation) or var1/2/3 (Blast against var123); and finally the coverage (coverage), pairwise identity (percentID), and e-value of the best BLAST hit (e-value).
Bioinformatic tools for marker gene sequencing data analysis are continuously and rapidly evolving, thus integrating most recent techniques and tools is challenging. We present an R package for data analysis of 16S and ITS amplicons based sequencing. This workflow is based on several R functions and performs automatic treatments from fastq sequence files to diversity and differential analysis with statistical validation. The main purpose of this package is to automate bioinformatic analysis, ensure reproducibility between projects, and to be flexible enough to quickly integrate new bioinformatic tools or statistical methods. rANOMALY is an easy to install and customizable R package, that uses amplicon sequence variants (ASV) level for microbial community characterization. It integrates all assets of the latest bioinformatics methods, such as better sequence tracking, decontamination from control samples, use of multiple reference databases for taxonomic annotation, all main ecological analysis for which we propose advanced statistical tests, and a cross-validated differential analysis by four different methods.
Introduction Studies of microbial communities tends to become a daily routine analysis for lots of laboratories and the main method to explore microbial diversity is metabarcoding, which is an amplicon targeted sequencing method (16S for bacteria and ITS for fungi). Metabarcoding generates a large amount of data and a lot of applications already exist for their processing (FROGS 1 , qiime  2  ). Methods and software are continuously evolving and the main challenge for bioinformaticians is to implement the most recent and effective ones in their analysis. Here we present rANOMALY, a scalable and lightweight R package which is able to handle every step of a metabarcoding analysis, from read cleaning, contaminant filtering, taxonomic assignment, to advanced statistical analysis. rANOMALY is fully implemented in R language in which each step correspond to one function, allowing to easy implementation of new features or tools while being easy to use and maintain.
Raw sequence processing Samples must have been previously demultiplexed into one file per sample with the file name following this syntax: {sampleid}_R [12]. fastq. The denoising process is handled using the dada2 R package  3  which produces amplicon sequence variants (ASV) as a taxonomic unit. This improves resolution of the potential presence of microbial organisms by using a prediction model to correct sequencing errors before aggregating similar sequences. rANOMALY handles processing any region of 16S (V1 to V9) and ITS amplicon (ITS1, ITS2) sequences, in which an additional step of cutadapt  4  removes ITS probes left in some short sequences. For 16S amplicon, primers are trimmed based on the primer sequence length. ASV identifiers are sequences translated into MD5 hashes which are unique identifiers based on the DNA sequences offering the possibility to be compared between projects.
Taxonomic assignment Taxonomic assignments of ASVs are carried out by IDTAXA (part of the DECIPHER package), an algorithm based on a machine learning method  5. We implemented a functionality to compute assignment with two reference databases. For example, 16S amplicon sequences can be assigned with an environment specific database (DAIRYdb 6 , HITdb  7  , MIDAS  8  ) and a general database like SILVA  9  or GreenGenes  10. The assignment with the best confidence and the lowest rank is kept, thus increasing assignment depth and accuracy. As taxonomy can differ between reference databases, we implemented a taxonomy validation step in rANOMALY to unravel taxonomy inconsistency like taxa with multiple ancestors or empty ranks. As supplementary features, rANOMALY functions allow users to create their own IDTAXA formatted specific reference database.
Phylogenetic tree Phylogenetic tree is generated in three steps. First, sequences are aligned with AlignSeqs function from DECI-PHER package  12  by the guided-tree method. Then, distance matrix is calculated with the dist. ml function from the phangorn package. And finally, neighbour joining and pml function computes the likelihood of the phylogenetic tree. The abundance, metadata, taxonomic table, reference sequences and phylogenetic tree are merged into a phyloseq object  13.
Metacoder outputs heat tree plots allowing users to infer differentially abundant features at each taxonomy rank and their position on the phylogenetic tree. With basic data management, and according to the identification of all significant differentially features (i. e. ASVs, genus, species) from the three methods, we generate a single table to find significant features in one or more methods and ease the interpretation. Additional information are added to the final table, like mean relative abundance for each feature and condition. A column in which condition features are significantly more abundant, features taxonomies and related sequences are added to the aggregated table. To complete differential analysis, we have included the well recognized PLS-DA from the mixOmics package  22. It is a supervised classification method allowing users to identify features discriminating the sample groups.
Additional features and export Functions and procedures are available to help the user to generate additional figures or to export the data to third party software. For instance, shared taxa between conditions are useful to explore. We use a function that can generate Venn diagrams, or for more complex visualisation, we can produce files readable by Cytoscape  23  to produce shared taxa networks. Krona diagrams can be displayed to explore sample microbial composition, where samples can be merged by a specified factor. rANOMALY allows users to generate inputs for STAMP  24  , which is a graphical software that provides statistical hypothesis tests and exploratory plots for analysing taxonomic profiles. Operation rANOMALY requires R 3. 6. 3 or upper and can be run on any operating system with common specifications (1Go disk space, 4Go RAM, multicore CPU is recommended).
Install Up to date code is hosted by the INRAE gitlab, users can simply download and install the package in R console with following command lines. # Install ranomaly install. packages("devtools") devtools::install_git("https://forgemia. inra. fr/umrf/ranomaly") # Call ranomaly package library(ranomaly) setwd("PATHTOWORKINGDIRECTORY") # Create the analysis directory dir. create(". /analysis").
fna fasta file with all representative sequences for each ASV. • We share the latest databases we use in the IDTAXA format in this link. Users can also generate your own IDTAXA formatted database following those instructions and scripts we provide at this page. # Taxonomy assignment step tax. table = assign_taxo_fun(dada_res = dada_res, id_db = c("PATHTO/SILVA_SSU_r138_2019. RData", "PATHTO/DAIRYdb_v1. 2. 4_20200604_IDTAXA. rdata"), verbose = 3, output = ". /analysis/02_idtaxa_silva_ddb") Main file outputs: • robjects. Rdata with taxonomy in phyloseq format in tax. table object. • final_tax_table. csv the final assignation table (tax. table) outputed in CSV format. • allDB_tax_table. csv raw assignations from the two databases, mainly for debugging.
Our function integrates the basic ASV frequency _ _ _ _ nb reads ASV Freq nb total reads =       and minimum prevalence in overall samples filtering. We have also included an option to filter out ASV based on their taxa names for known laboratory recurrent contaminants. Main outputs: • robjects. Rdata with contaminant filtered phyloseq object named data. • Exclu_out. csv list of filtered ASVs for each filtering step. • Krona plot before and after filtering. • raw_asv-table. csv and relative_asv-table. csv. Here we are going to filter out ASVs representing less than 0. 1% (freq) of the reads and that are present in more than 4 samples (prev). Moreover, we are excluding "unassigned" taxas for this use case. Our sample dataset do not contain control samples, this step will be skipped. data_filtered = decontam_fun(data = data, domain = "Bacteria", skip = TRUE, number = 4000, freq = 0.
($plot) • a table of indices values. ($alphatable) And for each of the computed indices : • an ANOVA analysis. (${measure}$anova) • a pairwise wilcox test result comparing conditions and giving the pvalue of each comparison tested: ${measure}$wilcox_col1: wilcox test results on the first or unique factor, ${measure} $wilcox_col2_fdr: wilcox test results on the second factor, ${measure}$wilcox_col2_ collapsed: wilcox test results on collapsed factor 1 and factor 2. • a mixture model if your dataset includes repeated measures, ie. column3 option. (${measure}$anovarepeat, ${measure}$mixedeffect) Beta diversity. The diversity_beta_light function allows to generate specific tests and figures ready to publish in rmarkdown report as in the example below. It is based on the vegan package function vegdist for the distance calculation and phyloseq-extended in addition to ordinate funtion for the ordination plot.
/analysis/08_tsv_table/{i}_ table/"), rank = i) } export_to_stamp_fun function creates two files that can be imported into the STAMP software. export_to_stamp_fun(data = data, output = ". /analysis/stamp/", correc = TRUE) csv2phyloseq_fun function allows user to import data from other bioinformatic pipeline like FROGS, Qiime2. This function needs tabulated ASV, taxonomy, metadata and DNA sequence table as inputs. It can generate phylogenetic tree if missing and output a phyloseq object ready for downstream analyses.
Conclusions rANOMALY allows users to handle metagenomic data from raw sequences quality control to final differential analysis with ready to publish results in an easy and reproducible manner. Users have access to all sources of the rANOMALY package that can be deployed any operating system or server allowing them to analyse anything from a few samples to several thousand. This workflow combines all of the latest developments in the field: the use of high resolution amplicon sequence variant, contaminant filtering, double automatic taxonomic assignation, integrated statistical analyses and four differential analyses with cross validation. rANOMALY help users those who don't have big programming skills. And since rANOMALY uses phyloseq objects and standards, original functions from the phyloseq package can still be used to split, filter and select specific samples previously to be visualized and/or tested with rANOMALY functions.
"? ○ p. 5 Install: The authors may want to add a "library(devtools)" line after the install of the package devtools and before installing the package ranomaly, in case some users already have the devtools package installed. The authors should also specify that a user will need to replace "PATHTOWORKINGDIRECTORY" with the path to their working directory on their own computer. (The same is true in the Taxonomic assignment section when users have to specify a path to the taxonomic databases). ○ p. 6 Processing of raw sequences: "files name match samples name" should be "file names match sample names" ○ p. 6 Taxonomic assignment: "givenby" should be "given by" "different genus" should be "different genera" "correction step" should be removed, or clarified as e. g. "as part of a correction step" "your own" should be "their own" p.