This is a collaboration with Pau Carazo and Zahida Sultanova. We are comparing the gut microbiome of several Drosophila melanogaster isolines, at two time points in their life cycle. We sequenced 16S rRNA gene amplicons and we are analyzing them with dada2.

Amparo suggested to use the linear discriminant analysis, implemented in the Galaxy tool Lefse, to identify biomarkers of age, or other categorical variables. In this folder I prepare the data and I link the results in the Galaxy session. The report is here and the Galaxy sessions are here and here. Pau suggested to use Lefse to explore the association of amplicon abundances with binary versions of principal components of life-history data. Some Acetobacter amplicons (but no genus) was detected significantly associated mostly with low quality values of PC1 and PC3.

Amparo suggested to include an abundance barplot, that I had avoided before, because of using too many amplicons, instead of a few taxa. Also, because the distribution is very skewed, and the plot will not look great. Here I do some barplots. I also use this folder to make a phylogenetic tree of the Acetobacter genus, highlighting the position of the amplicons declared significant by Lefse on 2020-05-01. Find a preview of the report here

Here I complete the analysis of diversity by exploring the relationship between age or isolines and beta diversity. Curiously, neither phylogenetic beta diversity of ASVs nor beta diversity of genera decrease with age, while simple presence-absence beta diversity (not phylogenetically informed) of ASVs does decrease with age. That is, microbiomes from different flies resemble more each other late than early in life if we look at ASV composition, but not if we take into account how similar sequences are.

Then I run again an RDA to correlate ASV abundance and life-history traits. This time I use only early abundances of ASVs that in 2020-03-17 proved to require an interaction term between age and isoline to adequately model their abundances. I apply a logarithmic transformation and keep only ASVs with very low skewness in their distribution of abundance across isolines (an RDA requirement). The 18 ASVs left prove significant in a global test, and I proceed with model selection. There seems to be two Acetobacter clades with negative effects on either life span or functional aging.

See a preview of the report here.

Summary for group members.

After updating results from the differential abundance analysis (2020-03-17), which also required updating the phyloseq analysis (2020-02-27), I made sure to use the same amplicon names in all datasets, and could merge the differential abundance results with other results, like diversity measures, in order to have more information about the amplicons. The idea was to improve the RDA by providing better quality, less noisy information. For example, the diversity measures, which had not been included in the RDA on 2020-04-21. Here I compute Faith's phylogenetic diversity index, which turns out to be very different between early and late samples.

Unfortunately, I have not been able to define, a priori, a set of variables based on amplicon abundances that can significantly explain variation in life-history traits through RDA. All models fail the first step, the global test. I can use the ordistep() function to let the algorithm pick the most relevant predictor variables, if I relax the significance threshold a bit. But I don't trust those results. In any case, even doing that, diversity does not come up as a significant variable.

The report is here

Full exploration of the RDA results, including all imputed datasets. The results are not positive. Only one principal component of log-transformed relative abundances is marginally significantly contributing to variation in life-history traits. But the amplicons that most heavily load on that component fail to explain any variation in life-history traits. If the marginally significant component is not a false positive, we can conclude that:

• Gut microbiome composition may affect life-history traits, but not because of the effect of any singular species. The effect, if real, seems to be combination of taxa.
• The life-history traits most likely to be influenced by gut microbiome are: the functional measures (climbing speed and its decline with age), and to some extent reproductive senescence. Neither the life-span nor the acceleration of mortality rate with age are much affected.

You can see the report here

Some additional work can be done to better characterize the marginally significant component of amplicon abundances. Maybe some kind of enrichment analysis could shed some light.

Upon realization that RDA cannot use all the amplicons' abundances, I resort to reducing the dimensionality of the dataset with PCA, and then use a subset of the principal components as explanatory variables in RDA. In order to keep the two isolines with missing values in one of the interesting life-history traits, I use multiple imputations. Preliminar results suggest there is not much variance in life-history traits that can be explained by variation in microbiome composition.

See the report here About how I chose the logarithmic transformation of relative abundance data, see this

Detailed ordination analysis. Here I learned about ordination analysis. I tested different distance measures to represent the variation among samples in two dimensions. I confirm that presence-absence data, rather than quantitative measures of abundance, allow a clearer distinction of what we consider potentially important sources of variation in two perpendicular axes: the age of the host flies, and the sequencing batch. I also convinced myself that double taxon absences should contribute to similarity among communities in our analysis, despite this not being the case in traditional applications of ordination in ecology. However, counting or not double absences does not change the display of samples in two dimensions.

Here I also decide that a redundancy analysis is what we need to determine how and to what extent bacterial taxa in the gut microbiome affect variation of life-history traits. I just set up the data matrices and make the analysis run, without getting into the details. See the report here

I use the DESeq2 package to test for different abundances between early and late microbiomes. At first, I followed a tutorial for phyloseq users, with very basic DESeq2 instructions. After reading more about DESeq2, I realize it offers likelihood ratio tests to compare different models. I have not seen this feature in EdgeR, for example. It makes me think that the right approach, instead of fitting the same model to all amplicons, is to separate first the amplicons by their most adequate model: those that respond to age from those that don't, and so on. Then, the most interesting amplicons are those that deserve the isoline term in the abundance model, or even better, those with an interaction term between isoline and age. Note that applying an LRT is like testing the significance of several terms simultaneously: I don't care if isoline 39 has an effect, but if any isoline does.

See this

I use phyloseq to combine abundance and taxonomy information, and to produce some plots. See the report here

I use dada2 to assign taxonomy to the amplicon sequence variants. Among 2683 sequences, only 26 are annotated to the species level. But 2419 (90%) are annotated to the genus, which is quite good. The distribution of phyla (not considering abundances, but just number of variants):

• Proteobacteria: 2122
• Firmicutes: 369
• Actinobacteriota: 84
• Bacteroidota: 43
• Cyanobacteria: 27
• Verrucomicrobiota: 3
• Deinococcota: 3
• Fusobacteriota: 3
• Acidobacteriota: 2
• Planctomycetota: 2
• Desulfobacterota: 2
• Chloroflexi: 1
• Gemmatimonadota: 1
• Patescibacteria: 1

See the report here.

I run dada2 on the whole dataset, pooling samples before calling the unique variants. It took around 20 hours. Unfortunately, I had to trim reads and remove those that still included Ns. The second sequencing batch produced a much more variable depth among samples; isolines 27 and 35 are under-sequenced early in life, while isoline 29 is badly over-sequenced late in life. See the report here and the alignment of the sequences here.

The second batch of sequences arrived. Three of the 28 combinations of isoline and time point lack replicates. There are large differences in number of reads and in merging success rate among samples. The dominant merged lengths are the same as in the first batch. See the report here. But, again, be aware that a table is not rendering properly in that preview.

I reproduce the plots and tables offered by qiime's visualization of the table of 'features' (a.k.a. exact sequence variants). I also produced a heatmap to visualize the whole abundance table. See the report here

Unfortunately, the tables from the reactable package do not work in the link above. Please, download the 2019-11-25/ExploreData.html file and open it in your browser.

There are 2472 exact sequence variants. The most frequent abundance is also the lowest, 2 observations in one sample. I suspect that a de-noising procedure with pooled samples would detect many more, low-frequency variants.

I imported the sequence data into qiime and de-noised the reads. I did it starting from both, the already merged reads, and the clean but not merged yet read pairs. This last way of processing the data is not necessary, but I was interested in checking how it works, and to evaluate the merged dataset offered by the sequencing services. I learned that dada2 trimming reads before merging and discarding reads shorter than the truncating point is bad practice, based on a flawed assumption: that fragments from which reads originated are all of very similar lengths. This contrasts with the merged data available, with two high-frequency lengths (440, 465). In this situation, it seems that dada2 default settings in qiime2 can introduce a bias in composition. See the report here

We got the fastq files from the first batch of amplicon sequencing. The provider run some preliminary analysis, and delivered a report on the results of the quality control process for every sample. Here, I pool all samples together to look at the numbers of reads and their length distributions along the pipeline. You can see a report here

k-mer analysis of some preliminar fly gut microbiota metagenomics data. My goal is to assess data quality and levels of diversity before assembly. You can see rendered versions of the reports in the following links:

• Analysis of sequence redundancy
• GC and k-mer frequency heatmaps
• k-mer frequency-based comparison among samples

Use freebayes with the four fastq files that contain unambiguously identified reads, in order to identify diagnostic SNPs. But I interrumpted this analysis here, because the real experiment of which this was a pilot got cancelled, for strategical reasons.

I want to call variants and estimate the potential heterozygosity among lines. I want to use both mpileup and freebayes, and maybe GATK as well.

I run step 1 of pyrad (demultiplexing), to compare the results with those of sabre, and to get an idea of how many more reads we can save from the undetermined fastq files. I also generate the histograms of merged read lengths.

I use bedtools to compare the bam files among the 4 samples. It is clear that sample 1, processed with only 12 PCR cycles, has a better distribution of coverage per site. All samples have more than 12000 sites covered at least 6 times, and more than 9000 of them are covered at least 6 times in all 4 samples.

Merged paired reads, trimmed adapters, and mapped them to the reference genome.

Demultiplexed the fastq files provided by the sequencing center. I am keeping track of the contradictions between the indices and the in-line barcode.

The goal here is to calculate the composition of the ligation reactions, in order to have a 10 fold excess of adapters to fragment ends

I use simuPop to simulate 20 generations of full-sib mating, and estimate the effect of inbreeding on the tracts of an autosome that are not identical by descent yet. The simulations reproduce the expected increase of inbreeding coeficient (F). However, the variance of F is high in general, and even higher in the absence of recombination in the male germline of Drosophila melanogaster. As a consequence of this variance, we expect almost 50% of flies inbred by full-sib mating for only 10 generations to be already completely homozygous.

Design of 12 adapters with in-line 8-nucloetide codewords, for use with restriction enzyme NspI.

The original goal was to check if the presence of X-specific sequences in Drosophila, involved in the gene-dose compensation system, allowed for an enrichment of X-chromosome fragments using restriction enzymes. The conclusion was negative. But I decided to keep pursuing a GBS experiment in Drosophila, and used the results to choose the most appropriate enzymes: NspI, and HaeII. I do not think we need more than 3000 fragments in the X chromosome

Assuming a yield of 20 million reads, and 20000 different genomic fragments, we could genotype 20 flies at 50X coverage in one MiSeq run.