Davis Geometric
Technical study / TESSERA / PARALLAX

TESSERA / assembly context and the implemented encoder

Research settingIndependent

TESSERA represents bacterial assemblies as a mosaic: a relatively persistent chromosomal context and potentially mobile components. It investigates whether a compositional mismatch can help locate changes worth biological review.

Source: PARALLAX TESSERA encoder and project description. Layer widths are source defaults; the assembly is symbolic, with no invented sequence or resistance measurement. Inspect the full technical study →

From the research record

Inspect the work

Original figures, recorded values, and explicit model details. Each exhibit identifies its source and what the comparison means.

01 / Source-code model definition

The implemented k-mer encoder, layer by layer

Scroll horizontally to inspect the full table →

The implemented k-mer encoder, layer by layer
Step Size / operation Meaning
Count sequence words k = 6; 4⁶ = 4,096 features Frequency vector over A, C, G, T words
Normalize counts Divide by the total counted words Removes total-count scale
Hidden layer 1 4,096 → 512; ReLU; dropout 0.2 Learned projection
Hidden layer 2 512 → 256; ReLU; dropout 0.2 Learned nonlinear representation
Output 256 → 128; L2 normalization Unit-length embedding

Default KmerEncoder configuration in the reviewed source. No trained weights or predictive accuracy are implied by these layer sizes. Its preprocessing removes N characters before counting, which can create artificial adjacency across ambiguous bases and needs evaluation.

tessera_encoder.py · KmerEncoder.__init__, count_kmers and forward
02 / Platform-defined tasks

Three outputs that must not share one accuracy claim

Scroll horizontally to inspect the full table →

Three outputs that must not share one accuracy claim
Output in the project Evidence type needed to evaluate it Unit of comparison
Outbreak clustering Independently supported outbreak / lineage labels Assembly or isolate
Resistance-gene detection Independent sequence annotation Gene or region
Horizontal-transfer tracking Supported mobile-element / transfer evidence Segment and genomic context

The task names come from PARALLAX; the evidence and comparison columns are the editorial evaluation map. No clinical resistance or transfer-rate measurements are present in this source.

README.md · TESSERA description and analysis gateway

Read the technical figure

Source: PARALLAX TESSERA encoder and project description. Layer widths are source defaults; the assembly is symbolic, with no invented sequence or resistance measurement.

Source: PARALLAX TESSERA encoder and project description. Layer widths are source defaults; the assembly is symbolic, with no invented sequence or resistance measurement.

Supporting detail

Complementary technical detail: Source: PARALLAX TESSERA encoder and project description. Layer widths are source defaults; the assembly is symbolic, with no invented sequence or resistance measurement.
Complementary technical detail: Source: PARALLAX TESSERA encoder and project description. Layer widths are source defaults; the assembly is symbolic, with no invented sequence or resistance measurement.

Why one embedding for an entire assembly can lose the question

The project descriptions use k-mer composition and a reduced representation of sequence windows. The proposed bundle separates a chromosomal backbone from plasmids and integrons. A segment whose composition departs from its surrounding context can then be examined as a possible mobile element rather than averaged into a whole-genome vector.

The older site calls this departure “shear.” That name identifies a proposed representation statistic. Its magnitude does not by itself date a transfer event or establish the donor, recipient, or mechanism.

Three outputs with different truth conditions

PARALLAX lists outbreak clustering, resistance-gene detection, and horizontal-transfer tracking under the bacterial analysis tool. A cluster of related assemblies, an annotated resistance determinant, and a measured resistant phenotype are three different outputs.

Assembly quality, lineage composition, and contamination can affect a geometric anomaly. Independent sequence annotations can help evaluate localization, while susceptibility measurements are needed for phenotype claims. A single threshold cannot validate all three tasks.

Sources, scope, and next question

Platform and representation descriptions; transfer events and resistance phenotypes require independent evidence.

A representative study should preserve segment-level predictions and compare them against independently annotated mobile regions, then evaluate resistance separately. Lineage-aware splits are necessary to show that the representation learns more than organism identity.

  1. PARALLAX project.

Additional source versions reviewed

  • parallax/README.md Local project manuscript or report · not republished here