The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

List of Top Authors of BMC Bioinformatics sorted by article citations.

List of Top Schools of BMC Bioinformatics sorted by citations.

The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

normalize1_withoutremove.zip is the time course of whole-brain activities of C. elegans for 28 animals used in Tsuyuzaki et al, "WormTensor: a clustering method for time-series whole-brain activity data from C. elegans” (BMC Bioinformatics, 2023, https://doi.org/10.1186/s12859-023-05230-2)This dataset is superset of the data used in Toyoshima et al, "Ensemble dynamics and information flow deduction from whole-brain imaging data" (now in press at PLoS Comp Biol, https://doi.org/10.1371/journal.pcbi.1011848) or its preprint on bioRxiv 2022.11.18.517011 (https://doi.org/10.1101/2022.11.18.517011) 20221118freezed.7z is the dataset used in with TDE-RICA (https://github.com/YuToyoshima/TDE-RICA) and gKDR-GMM (https://github.com/yuichiiino1/gKDR-GMM).

Through developmental plasticity, an individual organism integrates influences from its immediate environment with those due to the environment of its parents. While both effects on phenotypes are well documented, their relative impact has been little studied in natural systems, especially at the level of gene expression. We examined this issue in four genotypes of the annual plant Persicaria maculosa by varying two key resources light and soil moisture in both generations. Transcriptomic analyses showed that the relative effects of parent and offspring environment on gene expression (i.e., the number of differentially expressed transcripts, DETs) varied both for the two types of resource stress and among genotypes. For light, immediate environment induced more DETs than parental environment for all genotypes (although the precise proportion of parental versus immediate DETs varied among genotypes). By contrast, the relative effect of soil moisture varied dramatically among genotypes, from 8-fold more DETs due to parental than immediate conditions to 10-fold fewer. These findings provide evidence at the transcriptome level that the relative impacts of parental and immediate environment on the developing organism may depend on the environmental factor and vary strongly among genotypes, providing potential for the interplay of these developmental influences to evolve. Methods Parental and offspring generations We studied 4 genetic lines of Persicaria maculosa, an annual generalist plant of allotetraploid origin (Kim et al., 2008). Achenes from each of four experimental genotypes (MHF1, NAT1, NAT2, and TP2) were grown to reproductive maturity in one of three randomly assigned greenhouse treatments: full sun with moist soil (“High Light/Moist”), full sun with dry soil (“Dry”), or simulated shade with moist soil (“Shade”). Note that the parental High Light/Moisture treatment provided a stress-free (“control”) comparison for both the parental Dry and the parental Shade treatments. Mature achenes from one (self-fertilized) parent plant for each genotype × Parent treatment combination were germinated on petri plates and transplanted into pots (3 replicate seedings per pot). Experimental pots (4 genotypes × 5 [Parent treatment × Offspring treatment] combinations × 3 replicates = 60 pots) were raised in a randomized complete block design in a Conviron E2 growth chamber (Controlled Environments, Winnipeg, Canada) in one of 5 Parent treatment × Offspring treatment combinations—Parent High Light/Moist × Offspring High Light/Moist; Parent Shade × Offspring Shade; Parent Dry × Offspring Dry; Parent High Light/Moist × Offspring Shade; Parent High Light/Moist × Offspring Dry. 11-12 d post-transplant, leaf tissue from each replicate pot of 3 seedlings was harvested, pooled and flash frozen for RNA extraction (Promega SV Total RNA Isolation System Kit, Promega Corporation, Madison, WI, USA). De novo transcriptome sequencing, assembly, and annotation

We submitted all 60 RNA samples to the National Genomics Infrastructure (NGI) at Uppsala University, Uppsala, Sweden for RNA sequencing. Libraries were prepared for each sample using an Illumina TruSeq Stranded mRNA with Poly-A selection Library Prep kit (Illumina, San Diego, CA, USA), which were subsequently paired-end sequenced (2×150) on an Illumina NovaSeq 6000 platform utilizing an S1 flow cell. In addition to the short read sequences, we submitted a pool of 5 samples from genotype TP2 representing all 5 Parent/Offspring treatment combinations for long read sequencing following PacBio’s Iso-Seq protocol (Pacific Biosciences of California Inc., Menlo Park, CA, USA) using a PacBio Sequel sequencing platform at the NGI, Uppsala, Sweden. Because no reference genome of P. maculosa was available, we assembled the Illumina short read and PacBio long read data into a de novo transcriptome using Trinity software (version 2.8.4; Grabherr et al., 2011), following the protocol in Feiner et al. (Feiner et al., 2018), with minor changes made to optimize for this data set. Trinity assembled 48,022 transcripts, representing 33,828 predicted genes. The N50 for the transcriptome was 1,938 nucleotides (nt), with a median contig length of 1,015 nt and a mean contig length of 1,322.12 nt. We annotated the transcriptome using Trinotate (version 3.2.0; Bryant et al., 2017), a software suite that makes use of a variety of other annotation tools. In brief, TransDecoder (version 5.5.0, https://github.com/TransDecoder/TransDecoder) generated putative amino acid sequences, and BLASTx and BLASTp (BLAST+ version 2.9.0; Camacho et al., 2009) were used to search nucleic and amino acid sequences against the UniProtKB/Swiss-Prot database (retrieved December 19, 2019). A list of gene ontology (GO) terms for each transcript was generated based on the BLAST matches. Transcript quantification and differential expression analysis Transcript abundances were quantified with kallisto quant using default settings (Bray et al., 2016), and transcripts with low expression were discarded from the subsequent analyses. Complete descriptions can be found in the primary manuscript and the scripts contained within this respository. References Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology, 34, 525. doi:10.1038/nbt.3519 Bryant, D. M., Johnson, K., DiTommaso, T., Tickle, T., Couger, M. B., Payzin-Dogru, D., . . . Whited, J. L. (2017). A Tissue-Mapped Axolotl De Novo Transcriptome Enables Identification of Limb Regeneration Factors. Cell Reports, 18(3), 762-776. doi:10.1016/j.celrep.2016.12.063 Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009). BLAST+: architecture and applications. BMC Bioinformatics, 10, 421. doi:10.1186/1471-2105-10-421 Feiner, N., Rago, A., While, G. M., & Uller, T. (2018). Developmental plasticity in reptiles: Insights from temperature-dependent gene expression in wall lizard embryos. Journal of Experimental Zoology Part A: Ecological and Integrative Physiology, 329(6-7), 351-361. doi:10.1002/jez.2175 Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., . . . Regev, A. (2011). Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology, 29, 644. doi:10.1038/nbt.1883 Kim, S.-T., Sultan, S. E., & Donoghue, M. J. (2008). Allopolyploid speciation in Persicaria (Polygonaceae): Insights from a low-copy nuclear region. Proceedings of the National Academy of Sciences, 105(34), 12370-12375. doi:10.1073/pnas.0805141105

Methods: Barcoded and multiplexed genomic DNA libraries were prepared from purified DNA samples based on the GBS methodology published by Gilchrist et al. (2022) and Poland et al. (2012). Paired-end (2 9 150bp) Illumina NextSeq sequencing and variant calling were performed according to Gilchrist et al. (2022), using Trimmomatic (Bolger et al., 2014) for read trimming and the GBS-SNP-CROP 2.0 pipeline (Melo et al., 2016) for parsing, demultiplexing, alignment, and variant calling on the ASM23057v2 Purple Kush genome assembly (Laverty et al., 2019).List of accession names and flowering phenotype are provided in Supplemental Table S1Bolger, A.M., Lohse, M. & Usadel, B. (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30, 2114–2120. Available from: https://doi.org/10.1093/bioinformatics/btu170Gilchrist, E.J., Hegebarth, D., Wang, S., Quilichini, T.D., Sawler, J., Toh, S.Y. et al. (2022) A rapid method for sex identification in Cannabis sativa using high resolution melt analysis. Botany, 99,1–7. Available from: https://doi.org/10.1139/cjb-2021-0168Laverty, K.U., Stout, J.M., Sullivan, M.J., Shah, H., Gill, N., Holbrook, L. et al. (2019) A physical and genetic map of Cannabis sativa identifies extensive rearrangements at the THC/CBD acid synthase loci. Genome Research, 29, 146–156. Available from: https://doi.org/10.1101/gr.242594.118Melo, A.T., Bartaula, R. & Hale, I. (2016) GBS-SNP-CROP: a referenceoptional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data. BMC Bioinformatics, 17,1–15. Available from: https://doi.org/10. 1186/s12859-016-0879-yPoland, J.A., Brown, P.J., Sorrells, M.E. & Jannink, J.L. (2012) Development of high-density genetic maps for barley and wheat using a novel twoenzyme genotyping-by-sequencing approach. PLoS One, 7, e32253. Available from: https://doi.org/10.1371/journal.pone.0032253