Intersectional Framework

SARTools (Statistical Analysis of RNA-Seq data Tools) addresses these limitations by proposing a comprehensive, easy-to-use, DESeq2- and edgeR-based R pipeline that covers all the steps of a differential analysis, from the quality control of raw count data to the detection of differentially expressed genes. It applies to experimental designs involving one biological factor with two or more levels, such as time series or KO vs. WT experiments. When more than two levels are included in the design, all pairwise comparisons are performed. A blocking factor can be specified to take into account data pairing or the presence of a batch effect (e.g. day of preparation effect). However, SARTools does not handle complex experimental designs with interactions since it involves a careful definition of the design formula and of the contrasts to be tested according to the biological question under study. Indeed, it is neither desired nor safe to automate this part of the analysis process. Users who would have to analyse complex experimental designs are encouraged to use directly either DESeq2 or edgeR which both provide extensive help about this kind of experiments.
SARTools is composed of an R package and two R script templates that allow to run the analysis with either DESeq2 or edgeR. Both scripts rely on each package-specific functions as often as possible, and on SARTools functions to export figures and tables and to generate the HTML report. Each script starts with a section of about 15 parameters that refer to (i) paths to input files and the working directory where the analysis will be performed, (ii) project identification, (iii) experimental design, (iv) normalization and statistical test, (v) filtering process and (vi) plotting. Parameters (i) to (iii) have to be adapted to each analysis. The other parameters have default values and can be left unchanged but are accessible to advanced users if they wish to tune the analysis or the reporting more finely.
SARTools requires two types of input files: count data files containing raw counts and a target file that describes the experimental design [13 (link)]. Count data files are sample-specific and are composed of two columns (a unique feature identifier and a raw feature count) with no header. Note that the alignment and counting steps are out of the scope of SARTools and have to be carried out before using specific tools. HTSeq-count output files can be used as input for instance [14 (link)]. The target file contains one row per sample and at least three columns with headers: a unique sample identifier or label, the name of the associated raw counts file and the sample biological condition (see Table 1). If a blocking factor has to be accounted for (e.g. in case of batch effect or paired samples), it is reported in a fourth column. These input files are read by SARTools to build a matrix of integer values in which the intersection of the i-th row and the j-th column reflects how many reads have been mapped to feature i in sample j. This matrix is then used as input for DESeq2 or edgeR.
The source code of the package and instructions to quickly install it are available on GitHub (https://github.com/PF2-pasteur-fr/SARTools). Fig 1 describes the successive steps of the workflow and provides the names of the scripts and R functions corresponding to each step. Furthermore, the Galaxy wrappers to integrate SARTools into a Galaxy instance [15 (link)–17 (link)] are available on the Galaxy Tool Shed of the Institut Franҫais de Bioinformatique at http://toolshed.france-bioinformatique.fr/view/lgueguen/sartools_1_1_0. Galaxy is known to be very user-friendly for biologists and allows them to create worflows to deal with RNA-Seq data. Many tools were already available for the cleaning, mapping and counting steps and SARTools now offers the possibility to run the differential analysis step within the Galaxy environment.

The alignment anchors computed at node are used to perform an anchored profile-profile global alignment with modified MUSCLE 3.7 software [44] (link). Global profile-profile alignment requires the input sequences to be free from rearrangement. Therefore, we partition the anchors in into groups that are free from breakpoints in any pairwise projection. A fully fledged locally collinear block at node , no longer constrained to two dimensions, is a maximal set in which each pair-wise projection of into and in is contained in a common pair-wise LCB in . One or more of the original pair-wise LCBs from may be truncated by this restriction, and hence the partitioning into LCBs at node can be thought of as the intersection among constituent pairwise LCBs. Then each LCB in is independently subjected to anchored profile-profile alignment using methods described elsewhere [44] (link). In order to capture the full region of homology at the boundaries of each LCB, sequence regions outside LCBs are randomly split and assigned to neighboring LCBs. An example is shown with the yellow regions in Figure 2 step 5.
After the initial profile-profile alignment, we then apply window-based iterative refinement to improve the alignment. Step 6 of Figure 2 corresponds to this process. Importantly, MUSCLE refines the alignment with a multitude of alternative guide trees and is not restricted to the guide tree chosen for progressive anchoring. The use of multiple guide trees is a particularly important feature in microbial genomes, which are subject to lateral gene transfer. It should be noted that our use of MUSCLE as a refinement step is an approach used in other software pipelines as well [45] (link).

Gene differences analysis was performed (case vs control) in GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc), and the liver fibrosis standards were set as log fold change |logFC|> 1.5 and P < 0.05. There are 44,923 probe numbers listed in the GPL1261 platform, after mapping the probe into the gene, there were 21,722 genes in the results. GEO2R was used to plot the volcano of DEGs and the diagram of the intersection of DEGs and liver fibrosis. The DEGs were used for subsequent analysis [15 (link)].

Clinical assessments and analyses were performed on the patients. The American Orthopedic Foot and Ankle Society (AOFAS), Visual Analogue score (VAS), and SF-12 [21 (link)] were used to evaluate the function before the operation and at the last follow-up. The mental component score (MCS) and physical component score (PCS) were calculated using the SF-12 based on the Ware et al. Manual [22 (link)]. The final follow-up visits were performed from August to December 2022.
The foot was examined radiologically in the weight-bearing AP and lateral view. Calcaneal pitch angle, lateral Meary's angle, AP Meary's angle, AP talocalcaneal angle, and talonavicular coverage were measured twice by two different senior doctors at each visit (preoperative, three months after the operation and final follow-up). A successful fusion was defined as a painless foot during weight-bearing and trabeculation across the fusion line on radiography. These parameters measured on the weight-bearing AP and lateral views of the foot are shown in Fig. 2.Fig. 2

Measurement parameters on weight-bearing AP and lateral views. A The calcaneal pitch angle. B The lateral Meary's angle (positive sign = dorsal intersection; negative sign = plantar intersection). C Ap Meary's angle (positive sign = first metatarsal abduction; negative sign = adduction). D Ap talocalcaneal angle. E The talonavicular coverage (positive sign = navicular bone in valgus; negative sign = navicular bone in varus)